Publications

Original Peer-Reviewed Papers in Journals

Refereed Papers in Conference Proceedings

Invited/Review Articles in Journals/Magazines

Books/Book Chapters

5-Year Impact Factors (Impact Factors) of Journals by Thomson/ISI

IEEE Transactions on Medical Imaging 4.290 (3.390)
Medical Physics 2.954 (2.635)
Physics in Medicine and Biology 2.970 (2.761)
IEEE Transactions on Biomedical Engineering 2.570 (2.347)
IEEE Transactions on Pattern Analysis and Machine Intelligence 7.760 (5.781)
IEEE Transactions on Image Processing 4.480 (3.625)
IEEE Transactions on Signal Processing 3.440 (2.787)
Journal of Neural Engineering 3.740 (3.295)
Pattern Recognition 3.613 (3.096)
Radiology 7.259 (6.867)
European Journal of Nuclear Medicine and Molecular Imaging 5.094 (5.383)
Liver Transplantation 3.920 (4.241)
American Journal of Roentgenology 3.300 (2.731)
Journal of Magnetic Resonance Imaging 3.250 (3.210)
PLoS ONE 3.700 (3.234)

Citations by Google Scholar (as of 9/2017)

Number of citations: 8,448

h-index: 41
i10-index: 126

Original Peer-Reviewed Papers in Journals

In English

    1. Yisong Chen, Antoni B. Chan, Zhouchen Lin, Suzuki K., and Guoping Wanga: Efficient Tree-structured SfM by RANSAC Generalized Procrustes Analysis. Computer Vision and Image Understanding 157: 179-189, 2017. pdfminis
    2. Nima Tajbakhsh and Suzuki K.: Comparing Two Classes of End-to-End Learning Machines for Lung Nodule Detection and Classification: MTANNs vs. CNNs. Pattern Recognition 63: 476–486, 2017. pdfminis
    3. Huynh T. H., Ngoc T Le, Bao T Pham, Aytek Oto, and Suzuki K.: Fully automated MR Liver Volumetry using Watershed Segmentation Coupled with Active Contouring. International Journal of Computer Assisted Radiology and Surgery 12 (2): 235–243, 2017. pdfminis
    4. Amin Zarshenas and Suzuki K.: Binary Coordinate Ascent: An efficient optimization technique for feature subset selection for machine learning, Knowledge-Based Systems: 110: 191-201, 2016. pdfminis
    5. Shi Z., Ma J., Zhao M., Liu Y., Feng Y., Zhang M., He L., and Suzuki K.: Many is Better Than One: An Integration of Multiple Simple Strategies for Accurate Lung Segmentation in CT Images. BioMed Research International 2016: Article ID 1480423, 13 pages, 2016.
    6. Sihai Y., Xu J., Suzuki K.: Density Index: Extension of Shape Index in Describing Local Intensity Variations in a 3D Image, Journal of Computer-Aided Design & Computer Graphics 28 (6): 1-8, 2016.
    7. Chen S., Zhong S., Yao L., Yanfeng S., Suzuki K.: Enhancement of chest radiographs obtained in the intensive care unit through bone suppression and consistent processing, Physics in Medicine and Biology 61: 2283-2301, 2016. pdfminis
    8. Epstein M. L., Obara P. R., Chen Yi., Liu J., Zarshenas A., Makkinejad N., Dachman A. H., and Suzuki K.: Quantitative radiology: Automated measurement of polyp volume in computed tomography colonography using Hessian matrix-based shape extraction and volume growing. Quantitative Imaging in Medicine and Surgery 5: 673-684, 2015. pdfminis
    9. Dai P., Luo H., Sheng H., Zhao Y., Li L., Wu J., Zhao Y., Suzuki K.: A New Approach to Segment Both Main and Peripheral Retinal Vessels Based on Gray-voting and Gaussian Mixture Model, PLoS ONE 10(6): e0127748, 2015. pdfminis
    10. Shi Z., Ma J., Feng L., He L., and, Suzuki K.: Evaluation of MTANNs for eliminating false-positive with different computer aided pulmonary nodules detection software, Pakistan Journal of Pharmaceutical Sciences 28 (6): 2311-2316, 2015.
    11. Shi Z., Xu B., Zhao M., Zhao J., Wang Y., Liu Y., Zhang M. He L., and Suzuki K.: A joint ROI extraction filter for computer aided lung nodule detection. Bio-Medical Materials and Engineering 26: 1491-1499, 2015.
    12. Shi Z., Si C., Zhao M., He L., Zhang M., and Suzuki K.: An Automatic Method for Lung Segmentation in Thin Slice Computed Tomography Based on Random Walks. Journal of Medical Imaging and Health Informatics 5: 303-308, 2015.
    13. Shi Z., Si C., Feng Y., He L., and, Suzuki K.: A new method based on MTANNs for cutting down false-positives: An evaluation on different versions of commercial pulmonary nodule detection CAD software, Bio-Medical Materials and Engineering 24: 2839–2846, 2014.
    14. Yi-Xiáng J Wáng, Romaric Loffroy, Richa Arora, Suzuki K., Chang-Hee Lee, Hsiao-Wen Chung, Edwin H.G. Oei, Gavin P Winston, Chin K. Ng: Relative income of clinical faculty members vs. science faculty members in university settings-a short survey of France, Hong Kong, India, Japan, South Korea, The Netherlands, Taiwan, UK, and USA, Quantitative Imaging in Medicine and Surgery 24(6): 500–501, 2014.
    15. Wang Y., Gong J., Suzuki K., and Morcos S.K.: The Current Evidence Based Imaging Strategies for Solitary Pulmonary Nodule. Journal of Thoracic Disease 6(7): 872-887, 2014. pdfminis
    16. He L., Zhao X., Yao B., Yang Y., Chao Y., Shi Z., and Suzuki K.: A Combinational Algorithm for Connected-Component Labeling and the Euler Number Computing. Journal of Real-Time Image Processing, 2014. pdfminis
    17. Xu J., and Suzuki K.: Max-AUC Feature Selection in Computer-aided Detection of Polyps in CT Colonography. IEEE Journal of Biomedical and Health Informatics 18: 585-593, 2014 (Selected as a featured article on the cover page of the issue).
    18. Chen S. and Suzuki K.: Separation of Bones from Chest Radiographs by Means of Anatomically Specific Multiple Massive-Training ANNs Combined with Total Variation Minimization Smoothing. IEEE Transactions on Medical Imaging 33: 246-257, 2014.
    19. He L., Chao Y., and Suzuki K.: Configuration-Transition-Based Connected-Component Labeling. IEEE Transactions on Image Processing 23: 943-951, 2014.
    20. Huynh T. H., Karademir I., Oto A., and Suzuki K.: Computerized Liver Volumetry on MRI by Using 3D Geodesic Active Contour Segmentation. American Journal of Roentgenology 202: 152-159, 2014.
    21. Suzuki K.: Machine Learning in Computer-aided Diagnosis of the Thorax and Colon in CT: A Survey. IEICE Transactions on Information & Systems E96-D: 772-783, 2013 (Invited, peer-reviewed)(Awarded 2014 Best Paper Award from IEICE).
    22. He L., Chao Y., and Suzuki K.: An Algorithm for Connected-Component Labeling, Hole Labeling and Euler Number Computing. Journal of Computer Science and Technology 28(3): 468-478, 2013.
    23. Shi Z., Yang H., Zhao M., He L., Zhang M., and Suzuki K.: A Computer Aided Pulmonary Nodule Detection System Using Multiple Massive Training SVMs. Applied Mathematics & Information Sciences 7: 1165-1172, 2013.
    24. Chen S. and Suzuki K.: Computerized Detection of Lung Nodules by Means of “Virtual Dual-EnergyERadiography. IEEE Transactions on Biomedical Engineering 60: 369-378, 2013.
    25. El-Baz A., Beache G. M., Gimel’farb G., Suzuki K., Okada K., Elnakib A., Soliman A., and Abdollahi B.: Computer aided diagnosis systems for lung cancer: Challenges and methodologies.International Journal of Biomedical Imaging 2013: Article ID 942353, 46 pages, 2013.
    26. Shi Z., Zhao M., Wang Y., He L., Suzuki K., Jin C., and Zhang M.: Hessian-LoG: A Novel Dot Enhancement Filter. ICIC Express Letters 6: 1987-1992, 2012.
    27. Shi, Z., Li, L., Suzuki, K., Wang, Y., He, L., Jin, C., Zhang, M.: A New Computer Aided Detection System for Pulmonary Nodule Detection in Chest Radiography. Advanced Science Letters 11: 536-541, 2012.
    28. Yu Q., He L., Nakamura T., Chao Y., Suzuki K.: A Mutual-Information-Based Global Matching Method for Chest-Radiography Temporal Subtraction. Journal of Advanced Computational Intelligence and Intelligent Informatics 16: 841-850, 2012.
    29. Suzuki K.: A Review of Computer-aided Diagnosis in Thoracic and Colonic Imaging. Quantitative Imaging in Medicine and Surgery 2: 163-176, 2012 (Invited, peer-reviewed).
    30. He L., Chao Y., Suzuki K.: A New First-Scan Method for Two-Scan Labeling Algorithms. IEICE Transactions on Information and Systems E95-D: 2142-2145, 2012.
    31. Shi Z., Li L., Zhao M., He L., Wang Y., Zhang M., and Suzuki K.: Sparse Field Snake Model: A Novel Active Contour Model Used for Lung Segmentation on Chest Radiographs. ICIC Express Letters Part B: Applications 3: 777-783, 2012.
    32. Shi Z., Suzuki K., and He L., Improving the Accuracy of Computer Aided Nodules Detection in Chest Radiographs by Means of Neural Network Ensemble. Advanced Science Letters, 5, 400-407, 2012.
    33. Suzuki K.: Pixel-based Machine-Learning in Medical Imaging. International Journal of Biomedical Imaging 2012: Article ID 792079, 18 pages, 2012 (Invited, peer-reviewed).
    34. Liao S., Penney B. C., Zhang H., Suzuki K., and Pu Y.: Prognostic Value of the Quantitative Metabolic Volumetric Measurement on 18F-FDG PET/CT in Stage IV Nonsurgical Small-cell Lung Cancer. Academic Radiology 19: 69-77, 2012. (Top 6 Hottest Article in Academic Radiology in 2012)
    35. Liao S., Penney B. C., Wroblewski K., Zhang H., Simon C. A., Kampalath R., Shih M., Shimada N., Chen S., Salgia R., Appelbaum D. E., Suzuki K., Chen C., and Pu Y.: Prognostic Value of Metabolic Tumor Burden on 18F-FDG PET in Non-Surgical Patients with Non-Small Cell Lung Cancer. European Journal of Nuclear Medicine and Molecular Imaging 39: 27-38, 2011.
    36. Yu Q., He L., Nakamura T., Suzuki K., Chao Y.: A Multilayered Partitioning Image Registration Method for Chest-Radiograph Temporal Subtraction. American Journal of Engineering and Technology Research 11: 2422-2427, 2011.
    37. Chao Y., He L., Suzuki K.: A new connected-component labeling algorithm. American Journal of Engineering and Technology Research 11: 1099-1104, 2011.
    38. He L., Chao Y., Suzuki K., Yu Q., Tang W., Shi Z.: A Labeling Algorithm for Connected Components and Holes. American Journal of Engineering and Technology Research 11: 2149-2154, 2011.
    39. Hori M., Suzuki K., Epstein M. L., and Baron R. L.: Computed Tomography Liver Volumetry Using 3-Dimensional Image Data in Living Donor Liver Transplantation: Effects of the Slice Thickness on the Volume Calculation. Liver Transplantation, 17: 1427-1436, 2011.
    40. Suzuki K., Epstein M. L., Kohlbrenner R., Garg S., Hori M., Oto A., and Baron R. L.: Quantitative radiology: Automated CT liver volumetry compared with interactive volumetry and manual volumetry. American Journal of Roentgenology 197: W706-W712, 2011.
    41. Xu J., and Suzuki K.: Massive-training support vector regression and Gaussian process for false-positive reduction in computer-aided detection of polyps in CT colonography. Medical Physics 38: 1888-1902, 2011.
    42. Chen S., Suzuki K., and MacMahon H.: Development and evaluation of a computer-aided diagnostic scheme for lung nodule detection in chest radiographs by means of two-stage nodule-enhancement with support vector classification. Medical Physics 38: 1844-1858, 2011.
    43. He L., Chao Y., Suzuki K., and Nakamura T.: A new first-scan strategy for raster-scan-based labeling algorithms. Journal of Information Processing Society of Japan 52: 1813-1819, 2011.
    44. He L., Chao Y., and Suzuki K.: Two efficient label-equivalence-based connected-component labeling algorithms for three-dimensional binary images. IEEE Transactions on Image Processing 52: 1813-1819, 2011. pdfminis
    45. Shi Z., Bai J., Suzuki K., He L., Yao Q., and Nakamura T.: A method for enhancing dot-like regions in chest x-rays based on directional scale LoG filter, Journal of Information and Computational Science 7: 1689-1696, 2010.
    46. Suzuki K., Zhang J. and Xu J.: Massive-training artificial neural network coupled with Laplacian-eigenfunction-based dimensionality reduction for computer-aided detection of polyps in CT colonography. IEEE Transactions on Medical Imaging 29: 1907-1917, 2010.
    47. Lostumbo A., Suzuki K., and Dachman A. H.: Flat lesions in CT colonography. Abdominal Imaging 35: 578-583, 2010.
    48. He L., Chao Y., and Suzuki K.: A run-based one-and-a-half-scan connected-component labeling algorithm. International Journal of Pattern Recognition and Artificial Intelligence, 24:557-579, 2010.
    49. Suzuki K., Kohlbrenner R., Epstein M. L., Obajuluwa A. M., Xu J., and Hori M.: Computer-aided measurement of liver volumes in CT by means of geodesic active contour segmentation coupled with level-set algorithms. Medical Physics 37:2159-2166, 2010.
    50. He L., Chao Y., and Suzuki K.: An efficient first-scan method for pixel-based label-equivalence labeling algorithms. Pattern Recognition Letters 31: 28-35, 2010.
    51. Suzuki K., Rockey D. C., and Dachman A. H.: CT colonography: Advanced computer-aided detection scheme utilizing MTANNs for detection of “missed” polyps in a multicenter clinical trial. Medical Physics 30: 12-21, 2010.
    52. Lostumbo A., Wanamaker C., Tsai J., Suzuki K., and Dachman A. H.: Comparison of 2D and 3D views for evaluation of flat lesions in CT colonography. Academic Radiology 17: 39-47, 2010.
    53. He L., Chao Y., Suzuki K., Nakamura T., and Itoh H.: A high-speed labeling algorithm for three-dimensional binary images. Transactions of IEICE J92-D: 2261-2269, 2009.
    54. Hori M., Oto A., Orrin S., Suzuki K., Baron R. L.: Diffusion-weighted MR imaging: a new tool for the diagnosis of fistula in ano. Journal of Magnetic Resonance Imaging 30: 1021-1026, 2009.
    55. Oda S., Awai K., Suzuki K., Yanaga Y., Funama Y., MacMahon H., and Yamashita Y.: Detection of small pulmonary nodules on chest radiographs: Effect of rib suppression by the massive training artificial neural network (MTANN) technique on the performance of radiologists. American Journal of Roentgenology 193: W397–W402, 2009.
    56. Suzuki K.: Supervised ‘lesion-enhancementEfilter by use of a massive-training artificial neural network (MTANN) in computer-aided diagnosis (CAD). Physics in Medicine and Biology 54: S31-S45, 2009.
    57. Inaba T., He L., Suzuki K., Murakami K., and Chao Y.: A genetic-algorithm-based method for temporal subtraction of chest radiographs. Journal of Advanced Computational Intelligence and Intelligent Informatics 13: 289-296, 2009.
    58. He L., Chao Y., Suzuki K., Nakamura T., and Itoh H.: A label-equivalence-based one-scan labeling algorithm. Journal of Information Processing Society of Japan 50: 1660-1667, 2009.
    59. He L., Chao Y., Suzuki K., Nakamura T., and Itoh H.: A strategy for efficiency improvement of the first-scan in raster-scan-based labeling algorithms. Transactions of IEICE J92-D: 951-955, 2009.
    60. He L., Chao Y., Suzuki K., and Wu K.: Fast connected-component labeling. Pattern Recognition 42: 1977-1987, 2009.
    61. Shi Z., He L., Suzuki K., Nakamura T., Itoh H.: Survey of neural networks used in medical image processing. International Journal of Computer Science 3: 86-100, 2009.
    62. Wu K., Otoo E., and Suzuki K.: Optimizing two-pass connected-component labeling algorithms. Pattern Analysis and Applications 12: 117-135, 2009.
    63. He L., Chao Y., Suzuki K., Nakamura T., and Itoh H.: A run-based raster-scan labeling algorithm. Journal of the Institute of Image Information and Television Engineers 62: 1461-1465, 2008.
    64. He L., Chao Y., Suzuki K., Nakamura T., and Itoh H.: An efficient two-scan connected-component labeling algorithm. Transactions of IEICE J91-D: 1016-1024 2008.
    65. Shi Z., Chao Y., He L., Suzuki K., Nakamura T., and Itoh H.: Object location and track in image sequences by means of neural networks. International Journal of Computational Science 2: 274-285, 2008.
    66. He L., Chao Y., and Suzuki K.: A run-based two-scan labeling algorithm. IEEE Transactions on Image Processing 17: 749-756, 2008.
    67. Suzuki K., Yoshida H., Nappi J., Armato III S. G., and Dachman A. H.: Mixture of expert 3D massive-training ANNs for reduction of multiple types of false positives in CAD for detection of polyps in CT colonography. Medical Physics 35: 694-703, 2008.
    68. King M., Giger M. L., Suzuki K., Bardo D., Greenberg B., Lan L., and Pan X.: Computer-aided assessment of calcified plaques in cardiac computed tomography images. Medical Physics 34: 4876-4889, 2007.
    69. King M., Giger M. L., Suzuki K., and Pan X.: Feature-based characterization of calcified plaques in cardiac CT. Medical Physics 34: 4860-4875, 2007.
    70. He L., Chao Y., Suzuki K., Shi Z., and Itoh H.: An improvement on sub-Herbrand universe computation. The Open Artificial Intelligence Journal 1: 12-18, 2007.
    71. Yuan Y., Giger M. L., Li H., Suzuki K., and Sennett C.: A dual-stage method for lesion segmentation on digital mammograms. Medical Physics 34: 4180-4193, 2007.
    72. Chao Y., He L., Suzuki K., Nakamura T., Shi Z., and Itoh H.: An improvement of Herbrand theorem and its application to model generation theorem proving. Journal of Computer Science and Technology 22: 541-553, 2007.
    73. Muramatsu C., Li Q., Schmidt R. A., Shiraishi J., Suzuki K., Newstead G. M., and Doi K.: Determination of subjective similarity for pairs of masses and pairs of clustered microcalcifications on mammograms: comparison of similarity ranking scores and absolute similarity ratings. Medical Physics 34: 2890-2895, 2007.
    74. Doshi T., Rusinak D., Halvorsen B., Rockey D. C., Suzuki K., and Dachman A. H.: CT colonography: False-negative interpretations. Radiology 244: 165-173, 2007.
    75. Suzuki K., Yoshida H., Nappi J., and Dachman A. H.: Massive-training artificial neural network (MTANN) for reduction of false positives in computer-aided detection of polyps: suppression of rectal tubes. Medical Physics 33: 3814-3824, 2006.
    76. Muramatsu C., Li Q., Schmidt R. A., Suzuki K., Shiraishi J., Newstead G. M., and Doi K.: Experimental determination of subjective similarity for pairs of clustered microcalcifications on mammograms: Observer study results. Medical Physics 33: 3460-3468, 2006.
    77. Shiraishi J., Li Q., Suzuki K., Engelmann R., and Doi K.: Computer-aided diagnostic scheme for the detection of lung nodules on chest radiographs: localized search method based on anatomical classification. Medical Physics 33: 2642-2653, 2006.
    78. Suzuki K., Abe H., MacMahon H., and Doi K.: Image-processing technique for suppressing ribs in chest radiographs by means of massive training artificial neural network (MTANN). IEEE Transactions on Medical Imaging 25: 406-416, 2006. (Ranked among the top 100 most downloaded IEEE Xplore articles in January, 2008)
    79. Li F., Arimura H., Suzuki K., Shiraishi J., Li Q., Abe H., Engelmann R., Sone S., MacMahon H., and Doi K.: Computer-aided detection of peripheral lung cancers missed at CT: ROC analyses without and with localization. Radiology 237: 684-690, 2005.
    80. Li Q., Li F., Suzuki K., Shiraishi J., Abe H., Engelmann R., Nie Y., MacMahon H., and Doi K.: Computer-aided diagnosis in thoracic CT. Seminars in Ultrasound, CT and MRI 26: 357-363, 2005.
    81. Suzuki K., and Doi K.: How can a massive training artificial neural network (MTANN) be trained with a small number of cases in the distinction between nodules and vessels in thoracic CT? Academic Radiology 12: 1333-1341, 2005.
    82. Suzuki K., Li F., Sone S., and Doi K.: Computer-aided diagnostic scheme for distinction between benign and malignant nodules in thoracic low-dose CT by use of massive training artificial neural network. IEEE Transactions on Medical Imaging 24: 1138-1150, 2005.
    83. Muramatsu C., Li Q., Suzuki K., Schmidt R. A., Shiraishi J., Newstead G. M., and Doi K.: Investigation of psychophysical measure for evaluation of similar images for mammographic masses: Preliminary results. Medical Physics 32: 2295-2304, 2005.
    84. Suzuki K., Shiraishi J., Abe H., MacMahon H., and Doi K.: False-positive reduction in computer-aided diagnostic scheme for detecting nodules in chest radiographs by means of massive training artificial neural network. Academic Radiology 12: 191-201, 2005.
    85. Suzuki K.: Determining the receptive field of a neural filter. Journal of Neural Engineering 1: 228-237, 2004.
    86. Li F., Aoyama M., Shiraishi J., Abe H., Li Q., Suzuki K., Engelmann R., Sone S., MacMahon H., and Doi K.: RadiologistsEperformance for differentiating small benign from malignant lung nodules on high-resolution CT by using computer-estimated likelihood of malignancy. American Journal of Roentgenology 183: 1209-1215, 2004.
    87. Arimura H., Katsuragawa S., Suzuki K., Li F., Shiraishi J., Sone S., and Doi K.: Computerized scheme for automated detection of lung nodules in low-dose CT images for lung cancer screening. Academic Radiology 11: 617-629, 2004.
    88. Suzuki K., Horiba I., Sugie N., and Nanki M.: Extraction of left ventricular contours from left ventriculograms by means of a neural edge detector. IEEE Transactions on Medical Imaging 23: 330-339, 2004.
    89. Suzuki K., Horiba I., and Sugie N.: Neural edge enhancer for supervised edge enhancement from noisy images. IEEE Transactions on Pattern Analysis and Machine Intelligence 25: 1582-1596, 2003.
    90. Uchiyama Y., Katsuragawa S., Abe H., Shiraishi J., Li F., Li Q., Zhang C., Suzuki K., and Doi K.: Quantitative computerized analysis of diffuse lung disease in high-resolution computed tomography. Medical Physics 30: 2440-2454, 2003.
    91. Suzuki K., Armato III S. G., Li F., Sone S., and Doi K.: Massive training artificial neural network (MTANN) for reduction of false positives in computerized detection of lung nodules in low-dose CT. Medical Physics 30: 1602-1617, 2003. (Selected and published in an edited compilation, Virtual Journal of Biological Physics Research 6: 1, July 2003)
    92. Suzuki K., Horiba I., Sugie N., and Nanki M.: Contour extraction of the left ventricular cavity from digital subtraction angiograms using a neural edge detector. Systems and Computers in Japan 34: 55-69, 2003.
    93. Suzuki K., Horiba I., and Sugie N.: Linear-time connected-component labeling based on sequential local operations. Computer Vision and Image Understanding 89: 1-23, 2003. (Awarded Top 16 of Most Downloaded Articles Award)
    94. Suzuki K., Horiba I., Sugie N., and Nanki M.: Neural filter with selection of input features and its application to image quality improvement of medical image sequences. IEICE Transactions on Information and Systems E85-D: 1710-1718, 2002.
    95. Suzuki K., Horiba I., and Sugie N.: Efficient approximation of neural filters for removing quantum noise from images. IEEE Transactions on Signal Processing 50: 1787-1799, 2002.
    96. Suzuki K., Horiba I., and Sugie N.: A simple neural network pruning algorithm with application to filter synthesis. Neural Processing Letters 13: 43-53, 2001.
    97. Suzuki K., Horiba I., and Sugie N.: An approach to synthesize filters with reduced structures using a neural network. Quantum Information 2: 205-218, 2000.
    98. Suzuki K., Horiba I., Ikegaya K., and Nanki M.: Recognition of coronary arterial stenosis using neural network on DSA system. Systems and Computers in Japan 26: 66-74, 1995.

In Japanese

  1. Suzuki K.: Supervised nonlinear image processing based on artificial neural networks: Basic principle of neural image processing and its applications. Japanese Journal of Nuclear Medicine Technology 24: 433-442, 2004.
  2. Suzuki K., Horiba I., and Sugie N.: Detection of edges in noisy images using a neural edge detector. Transactions of IEICE J86-D-II: 579-583, 2003.
  3. Ninagawa K., Umeyama T., Suzuki K., and Sugie N.: Sound source separation in the frequency domain with image processing. Transactions of Institute of Electrical Engineers of Japan 121-C: 1866-1874, 2001. (Awarded Best Paper Award for Young Researchers)
  4. Suzuki K., Horiba I., Sugie N., and Nanki M.: Neural filter with selection of input features for improving image quality of medical x-ray image sequences. Journal of Information Processing Society of Japan 42: 2176-2188, 2001.
  5. Suzuki K.: Studies on neural image processing for medical x-ray images. PhD Thesis, Graduate School of Engineering, Nagoya University, 1503, 2001.
  6. Suzuki K., Horiba I., and Sugie N.: Fast connected-component labeling through sequential local operations in the course of forward raster scan followed by backward raster scan. Journal of Information Processing Society of Japan 41: 3070-3081, 2000.
  7. Suzuki K., Horiba I., Sugie N., and Nanki M.: Contour extraction of left ventricles in DSA images by means of neural edge detector. Transactions of IEICE J83-D-II: 2017-2029, 2000.
  8. Suzuki K., Horiba I., and Sugie N.: An analysis of the neural filter trained to improve quality of images with quantum noise and realization of approximate filter. Journal of Information Processing Society of Japan 41: 711-721, 2000.
  9. Suzuki K., Horiba I., and Sugie N.: A method for determining reduced structure of a neural filter. Journal of Information Processing Society of Japan 40: 4226-4238, 1999.
  10. Suzuki K., Hayashi T., Ikeda S., Horiba I., and Sugie N.: Improving image quality of medical low-dose x-ray image sequences using a neural filter. Transactions of Institute of Electrical Engineers of Japan 119-C: 1383-1391, 1999.
  11. Ueda K., Yamada M., Horiba I., Ikegaya K., and Suzuki K.: A direct estimation method of occupancy rate in parking lot using analogue output neural network model. Journal of Information Processing Society of Japan 36: 627-635, 1995.
  12. Suzuki K., Horiba I., Ikegaya K., and Nanki M.: Recognition of degree of stenosis using neural network on coronary arterial DSA system. Transactions of IEICE J77-D-II: 1910-1916, 1994.


↑ Go to top

Refereed Papers in Conference Proceedings

In English

    1. He L., Chao Y., Yang Y., Li S., Suzuki K.: A Novel Two-Scan Connected-Component Labeling Algorithm, IAENG Transactions on Engineering Technologies, Lecture Notes in Electrical Engineering, 229: xx-xx, 2013 (in press).
    2. Suzuki K., Huynh H. T., Liu Y., Calabrese D., Zhou K., Oto A., Hori M.: Computerized Segmentation of Liver in Hepatic CT and MRI by Means of Level-Set Geodesic Active Contouring, Proc. IEEE Engineering in Medicine and Biology Conference (IEEE EMBC), 2984-2987, Osaka, Japan, July 2013 (Invited).
    3. Calabrese D., Zhou K., Liu Y., Suzuki K.: Improved Segmentation of Liver in CT with Massive-Training Artificial Neural Network (MTANN) Liver Enhancer, Proc. IEEE Engineering in Medicine and Biology Conference (IEEE EMBC), Short Papers No. 3331, Osaka, Japan, July 2013.
    4. Suzuki K., Hori M., Iinuma G., and Dachman A. H.: Effect of CADe on RadiologistsEPerformance in Detection of “DifficultEPolyps in CT Colonography. Proc. SPIE Medical Imaging (SPIE MI), 8670: 8670x-1-x, Orlando, FL, February 2013.
    5. He L., Chao Y., Suzuki K.: A New Algorithm for Labeling Connected-Components and Calculating the Euler Number, Connected-Component Number, and Hole Number, Proc. Int. Conf. Pattern Recognition (ICPR), 3099-3102, Tsukuba, Japan, November 2012.
    6. Cheng S., Suzuki K.: Bone Suppression in Chest Radiographs by Means of Anatomically Specific Multiple Massive-Training ANNs, Proc. Int. Conf. Pattern Recognition (ICPR), 17-20, Tsukuba, Japan, November 2012.
    7. Yu Q., He L., Chao Y., Suzuki K., Nakamura T.: A mutual-information-based image registration method for chest-radiograph temporal subtraction. International Conference on Computer Science and Information Processing (CSIP), 1098-1101, Nagoya, Japan, August 2012.
    8. Yu Q., He L., Nakamura T., Suzuki K., Chao Y.: A multilayered partitioning image registration method for chest-radiograph temporal subtraction. International Conference on Computer Science and Information Processing (CSIP), 181-184, Nagoya, Japan, August 2012.
    9. He L., Chao Y., Suzuki K.; A New Two-Scan Algorithm for Labeling Connected Components in Binary Images, World Congress on Engineering, July, London, 2012.
    10. Xu J., and Suzuki K.: Maximal Partial AUC Feature Selection in Computer-Aided Detection of Hepatocellular Carcinoma in Contrast-Enhanced Hepatic CT. Proc. SPIE Medical Imaging (SPIE MI), 8315: 83150H-1-7, San Diego, CA, February 2012.
    11. Xu J., and Suzuki K.: Computer-aided Detection of Polyps in CT Colonography By Means of AdaBoost. Proc. SPIE Medical Imaging (SPIE MI), 8315: 83150V-1-7, San Diego, CA, February 2012.
    12. Chao Y., He L., Suzuki K.: A new connected-component labeling algorithm, Proceedings of 2011 International Conference on Opto-Electronics Engineering and Information Science (ICOEIS2011), xxx-xxx, Xi’an, China, December 2011.
    13. He L., Chao Y., Suzuki K.: A Labeling Algorithm for Connected Components and Holes, Proceedings of 2011 International Conference on Opto-Electronics Engineering and Information Science (ICOEIS2011), xxx-xxx, Xi’an, China, December 2011.
    14. Xu J. and Suzuki K.: False-positive reduction in computer-aided detection of polyps in CT colonography: a massive-training support vector regression approach. Lecture Notes in Computer Science, Virtual Colonoscopy and Abdominal Imaging 6668: 47E2 (Springer-Verlag, Berlin), 2011.
    15. Suzuki K.: Recent Advances in False-Positive Reduction Methods in CAD for CTC. Lecture Notes in Computer Science, Virtual Colonoscopy and Abdominal Imaging 6668: 32E9 (Springer-Verlag, Berlin), 2011. (Invited)
    16. Xu J. and Suzuki K.: Computer-Aided Detection of Polyps in CT Colonography with Pixel-based Machine Learning Techniques, Lecture Notes in Computer Science, Machine Learning in Medical Imaging (MLMI) 7009: 360E68 (Springer-Verlag, Berlin), Toronto, Canada, September 2011.
    17. Suzuki K.: Computer-aided diagnosis – research, development, commercialization and clinical implementation. Proceedings of Workshop on Fusion of Information Technology and Medicine, pp. 5-14, Shiga, Japan, August 2011.
    18. Xu J., Suzuki K.: Computer-aided detection of hepatocellular carcinoma in hepatic CT: False positive reduction with feature selection, IEEE International Symposium on Biomedical Imaging (IEEE ISBI), 1097-1100, Chicago, March 2011.
    19. Ferraro F., Kawaler E., Suzuki K.: A spinning tangent based CAD system for detection of flat lesions in CT colonography, IEEE International Symposium on Biomedical Imaging (IEEE ISBI), 156-159, Chicago, March 2011.
    20. Yu Q., He L., Nakamura T., Suzuki K., Chao Y.: A Mutual-Information-Based Image Registration Method for Chest-Radiograph Temporal Subtraction, 2011 3rd IEEE International Conference on Computer and Network Technology (ICCNT 2011), V13-359- V13-362, Taiyuan, China, February 2011.
    21. Chen S., Suzuki K., and MacMahon H.: Improved computerized detection of lung nodules in chest radiographs by means of “virtual dual-energyE radiography. Proc. SPIE Medical Imaging (SPIE MI), 7963: 79630C, Orlando, FL, February 2011.
    22. Xu J., Suzuki K., Hori M., Oto A., and Baron R.: Computer-aided detection of hepatocellular carcinoma in multiphase contrast-enhanced hepatic CT: a preliminary study. Proc. SPIE Medical Imaging (SPIE MI), 7963: 79630S, Orlando, FL, February 2011.
    23. Suzuki K., Armato S. G., Engelmann R., Caligiuri P., and MacMahon H.: Temporal subtraction of “virtual dual-energyEchest radiographs for improved conspicuity of growing cancers and other pathologic changes. Proc. SPIE Medical Imaging (SPIE MI), 7963: 79630F, Orlando, FL, February 2011.
    24. Xu J. and Suzuki K.: False-positive reduction in computer-aided detection of polyps in CT colonography: a massive-training support vector regression approach. Proc. MICCAI 2010 Workshop on Computational Challenges and Clinical Opportunities in Virtual Colonoscopy and Abdominal Imaging, 55-60, Beijing, China, September 2010.
    25. Suzuki K.: Recent advances in false-positive reduction methods in CAD for CTC. Proc. MICCAI 2010 Workshop on Computational Challenges and Clinical Opportunities in Virtual Colonoscopy and Abdominal Imaging, 41-48, Beijing, China, September 2010. (Invited)
    26. Suzuki K., Xu J., and Sheu I.: Principal-component massive-training machine-learning regression for false-positive reduction in computer-aided detection of polyps in CT colonography. Lecture Notes in Computer Science, Machine Learning in Medical Imaging (MLMI) 6357: 182E89 (Springer-Verlag, Berlin), Beijing, China, September 2010.
    27. He L., Inaba T., Suzuki K., Murakami K., Chao Y., Tang W., Shi Z., Nakamura T., A global registration method for temporal subtraction of chest radiographs, Proc. 2010 International Conference on Image Processing and Pattern Recognition in Industrial Engineering, Proc. SPIE, 7820: 78202A1-78202A8, Xian, Shaanxi, China, 2010.
    28. He L., Chao Y., Suzuki K., Tang W., Shi Z., Nakamura T., An efficient run-based connected-component labeling algorithm for three-dimensional binary images, Proc. 2010 International Conference on Image Processing and Pattern Recognition in Industrial Engineering, Proc. SPIE, 7820: 7820291-7820298, Xian, Shaanxi, China, 2010.
    29. Shi Z., Suzuki K., and He L.: A filtering method for enhancing dot-like regions in chest x-rays. Proc. the 8th International Bioinformatics Workshop (IBW2010), xx: xx-xx, Wuhan, China, 2010.
    30. Suzuki K., Epstein M. L., Xu J., Obara P. R., Rockey D. C., and Dachman A. H.: Automated scheme for measuring polyp volume in CT colonography using Hessian matrix-based shape extraction and 3D volume growing. Proc. SPIE Medical Imaging (SPIE MI), 7624: 762423-1-6, San Diego, 2010.
    31. Suzuki K., Epstein M. L., Kohlbrenner R., Obajuluwa A. M., Xu J., Hori M., and Baron R. CT liver volumetry using 3D geodesic active contour segmentation with a level-set algorithm. Proc. SPIE Medical Imaging (SPIE MI), 7624: 76240R-1-6, San Diego, 2010.
    32. Shi Z., Suzuki K., and He L.: Reducing FPs in nodule detection using neural networks ensemble. Second International Symposium on Information Science and Engineering (ISISE), pp. 331 E333, Shanghai, China, December 2009.
    33. He L., Chao Y., Suzuki K., and Itoh H.: A fast first-scan algorithm for label-equivalence-based connected-component labeling. Proc. IEEE International Conference on Image Processing (IEEE ICIP), Cairo, Egypt, November 2009.
    34. Suzuki K., Hori M., McFarland E. G., Friedman A. C., Iinuma G., Rockey D. C., and Dachman A. H.: Observer performance study with CAD in detection of polyps in false-negative cases: Preliminary results. Proc. International Symposium on Virtual Colonoscopy (ISVC), p. 135, Reston, VA, October 2009.
    35. He L., Chao Y., Suzuki K., and Itoh H.: A run-based one-scan labeling algorithm. Lecture Notes in Computer Science, Image Analysis and Recognition (ICIAR) 5627: 93-102, (Springer-Verlag, Berlin), Halifax, Canada, July 2009.
    36. Suzuki K., Sheu I., Rockey D. C., and Dachman A. H.: A CAD utilizing 3D massive-training ANNs for detection of flat lesions in CT colonography: Preliminary results. Proc. SPIE Medical Imaging (SPIE MI), 7260: 72601A-1-7, Orlando, FL, February 2009.
    37. Suzuki K.: Segmentation of lesions with improved specificity in computer-aided diagnosis using a massive-training artificial neural network (MTANN). Proc. Int. Conf. Machine Learning and Applications (ICMLA), pp. 523-527, San Diego, CA, December 2008.
    38. Suzuki K., Shi Z., and Zhang J.: Supervised enhancement of lesions by use of a massive-training artificial neural network (MTANN) in computer-aided diagnosis (CAD). Proc. Int. Conf. Pattern Recognition (ICPR), MoCT6.3, 4 pages, Tampa, FL, December 2008.
    39. Suzuki K., Sheu I., Rockey D. C., and Dachman A. H.: Detection of flat lesions: Performance of a CAD utilizing 3D massive-training ANNs on a cohort from a large multicenter clinical trial. Proc. International Symposium on Virtual Colonoscopy (ISVC), pp. 152-153, Boston, MA, October 2008.
    40. Suzuki K., Epstein M. L., Kuo J., Obara P. R., Rockey D. C., and Dachman A. H.: CT colonography polyp volumetrics: Fully automated scheme for measuring polyp volume using 3D volume-growing and sub-voxel refinement techniques. Proc. International Symposium on Virtual Colonoscopy (ISVC), pp. 149-150, Boston, MA, October 2008.
    41. Inaba T., He L., Chao Y., Suzuki K., and Murakami K: A genetic-algorithm-based method for temporal subtraction in chest radiography. Proc. Joint 4th International Conference on Soft Computing and Intelligent Systems and 9th International Symposium on advanced Intelligent Systems (SCIS & ISIS), pp. 1619-1624, Nagoya, Japan, September 2008.
    42. He L., Chao Y., Suzuki K., Nakamura T., and Itoh H.: A survey of labeling algorithms. Proc. Joint 4th International Conference on Soft Computing and Intelligent Systems and 9th International Symposium on advanced Intelligent Systems (SCIS & ISIS), pp.1293-1298, Nagoya, Japan, September 2008 (Invited).
    43. Suzuki K., Epstein M. L., Sheu I., Kohlbrenner R., Rockey D. C., and Dachman A. H.: Massive-training artificial neural networks for cad for detection of polyps in CT colonography: False-negative cases in a large multicenter clinical trial.Proc. IEEE International Symposium on Biomedical Imaging (IEEE ISBI), pp. 684-687, Paris, France, May 2008.
    44. Suzuki K., Sheu I., Epstein M. L., Kohlbrenner R., Lostumbo A., Rockey D. C., and Dachman A. H.: An MTANN CAD for detection of polyps in false-negative CT colonography cases in a large multicenter clinical trial: Preliminary results. Proc. SPIE Medical Imaging (SPIE MI), 6915: 69150F-1-7, San Diego, CA, February 2008.
    45. Rodgers Z. B., King M. T., Giger M. L., Bardo D., Vannier M. W., Lan L., and Suzuki K.: Computerized assessment of coronary calcified plaques in CT images of a dynamic cardiac phantom. Proc. SPIE Medical Imaging (SPIE MI), 6915: 69150M-1-6, San Diego, CA, February 2008.
    46. King M., Giger M. L., Suzuki K., and Pan X.: Image quality evaluation of motion-contaminated calcified plaques in cardiac CT. IEEE Nuclear Science Symposium Conference Record, pp. 2717-2720, Honolulu, HI, October 2007.
    47. Lostumbo A., Tsai J., Suzuki K., and Dachman A. H.: Comparison of 2D and 3D views for measurement and conspicuity of flat lesions in CT colonography. Proc. International Symposium on Virtual Colonoscopy (ISVC), pp. 120-121, Boston, MA, October 2007.
    48. Suzuki K., Sheu I., Epstein M. L., Verceles J., Rockey D. C., and Dachman A. H.: Performance of CAD based on MTANNs for detection of false-negative polyps in a multicenter clinical trial. Proc. International Symposium on Virtual Colonoscopy (ISVC), pp. 93-94, Boston, MA, October 2007.
    49. He L., Chao Y., and Suzuki K.: A linear-time two-scan labeling algorithm. IEEE International Conference on Image Processing (IEEE ICIP), V: 241-244, San Antonio, TX, September 2007.
    50. He L., Chao Y., and Suzuki K.: A run-based two-scan labeling algorithm. Lecture Notes in Computer Science, International Conference on Image Analysis and Recognition (ICIAR) 4633: 131E42 (Springer-Verlag, Berlin), Montreal, Canada, August 2007.
    51. Muramatsu C., Li Q., Schmidt R. A., Shiraishi J., Suzuki K., Newstead G. M., and Doi K.: Determination of subjective and objective similarity for pairs of masses on mammograms for selection of similar images. Proc. SPIE Medical Imaging (SPIE MI), 6514, 65141I-1-9, 2007.
    52. King M., Pan X., Giger M. L., and Suzuki K.: Motion compensated reconstructions of calcified coronary plaques in cardiac CT. Proc. SPIE Medical Imaging (SPIE MI), 6510, 651012-1-6, 2007.
    53. King M., Giger M. L., Suzuki K., and Pan X.: Computer-aided assessment of cardiac computed tomography images. Proc. SPIE Medical Imaging (SPIE MI), 6514, 65141B-1-6, 2007.
    54. Suzuki K., He L., Khankari S., Ge L., Verceles J., and Dachman A. H.: Mixture of expert artificial neural networks with ensemble training for reduction of various sources of false positives in CAD. Proc. SPIE Medical Imaging (SPIE MI), 6514, 651401-1-6, 2007.
    55. Li H., Giger M. L., Yuan Y., Lan L., Suzuki K., Jamieson A. R., Yarusso L., Nishikawa R. M., and Sennett C.: Comparison of computerized image analyses for digitized screen-film mammograms and full-field digital mammography images. Lecture Notes in Computer Science, Digital Mammography 4046: 569-575 (Springer-Verlag, Berlin), 2006.
    56. Suzuki K., Li F., Li Q., MacMahon H., and Doi K.: Comparison between 2D and 3D massive-training ANNs (MTANNs) in CAD for lung nodule detection on MDCT. International Journal of Computer Assisted Radiology and Surgery 1(p): 354-355, 2006.
    57. Yuan Y., Giger M. L., Suzuki K., Li H., and Jamieson A. R.: A two-stage method for lesion segmentation on digital mammograms. Proc. SPIE Medical Imaging (SPIE MI) 6144, 2006. (Awarded Honorable Mention Poster Award)
    58. Wu K., Otoo E., and Suzuki K.: Two Strategies to speed up connected component labeling algorithms. Lawrence Berkeley National Laboratory Tech Report LBNL-59102, 2005.
    59. Suzuki K., Li F., Aoyama M., Shiraishi J., Abe H., Li Q., Engelmann R., Sone S., MacMahon H., and Doi K.: Effect of CAD on radiologistsEresponses in distinction between malignant and benign pulmonary nodules on high-resolution CT. Proc. SPIE Medical Imaging (SPIE MI) 5749: 502-507, 2005.
    60. Suzuki K., Shiraishi J., Li F., Abe H., MacMahon H., and Doi K.: Effect of massive training artificial neural networks for rib suppression on reduction of false positives in computerized detection of nodules on chest radiographs. Proc. SPIE Medical Imaging (SPIE MI) 5747: 97-103, 2005.
    61. Li F., Li Q., Aoyama M., Shiraishi J., Abe H., Suzuki K., Engelmann R., Sone S., MacMahon H., and Doi K.: Usefulness of computerized scheme for differentiating benign from malignant lung nodules on high-resolution CT. Computer Assisted Radiology and Surgery (CARS) pp. 946-951, 2004.
    62. Suzuki K. and Doi K.: Characteristics of a massive training artificial neural network in the distinction between lung nodules and vessels in CT images. Computer Assisted Radiology and Surgery (CARS) pp. 923-928, 2004.
    63. Suzuki K., Abe H., Li F., and Doi K.: Suppression of the contrast of ribs in chest radiographs by means of massive training artificial neural network. Proc. SPIE Medical Imaging (SPIE MI) 5370: 1109-1119, 2004.
    64. Suzuki K., Armato III S. G., Li F., Sone S., and Doi K.: Effect of a small number of training cases on the performance of massive training artificial neural network (MTANN) for reduction of false positives in computerized detection of lung nodules in low-dose CT. Proc. SPIE Medical Imaging (SPIE MI) 5032: 1355-1366, 2003.
    65. Suzuki K., Horiba I., and Sugie N.: Simple unit-pruning with gain-changing training. Proc. IEEE Int. Workshop on Neural Networks for Signal Processing (NNSP) XI: 153-162, 2001.
    66. Ninagawa K., Umeyama T., Suzuki K., and Sugie N.: Voice separation in the frequency domain using image processing. Proc. Int. Conf. Software Engineering, Artificial Intelligence, Networking & Parallel/Distributed Computing (SNPD) pp. 746-753, 2001.
    67. Ninagawa K., Umeyama T., Suzuki K., and Sugie N.: Sound source separation in the frequency domain with image processing. Human-Computer Interaction (INTERACT) pp. 781-782, 2001.
    68. Suzuki K., Horiba I., and Sugie N.: Neural edge detector -a good mimic of conventional one yet robuster against noise-. Lecture Notes in Computer Science 2085: 303-310, 2001.
    69. Suzuki K., Horiba I., Sugie N., and Nanki M.: Computer-aided diagnosis system for coronary artery stenosis using a neural network. Proc. SPIE Medical Imaging (SPIE MI) 4322: 1771-1782, 2001.
    70. Suzuki K., Horiba I., Sugie N., and Nanki M.: Extraction of the contours of left ventricular cavity, according with those traced by medical doctors, from left ventriculograms using a neural edge detector. Proc. SPIE Medical Imaging (SPIE MI) 4322: 1284-1295, 2001.
    71. Suzuki K., Horiba I., and Sugie N.: Training under achievement quotient criterion. Proc. IEEE Int. Workshop on Neural Networks for Signal Processing (NNSP) X: 537-546, 2000.
    72. Suzuki K., Horiba I., and Sugie N.: Edge detection from noisy images using a neural edge detector. Proc. IEEE Int. Workshop on Neural Networks for Signal Processing (NNSP) X: 487-496, 2000.
    73. Suzuki K., Horiba I., and Sugie N.: Neural filter with selection of input features and its application to image quality improvement of medical image sequences. Proc. IEEE Int. Symp. Intelligent Signal Processing and Communication Systems (ISPACS) II: 783-788, 2000.
    74. Suzuki K., Horiba I., and Sugie N.: Signal-preserving training for neural networks for signal processing. Proc. IEEE Int. Symp. Intelligent Signal Processing and Communication Systems (ISPACS) I: 292-297, 2000.
    75. Suzuki K., Horiba I., and Sugie N.: Fast connected-component labeling based on sequential local operations in the course of forward raster scan followed by backward raster scan. Proc. Int. Conf. Pattern Recognition (ICPR) 2: 434-437, 2000.
    76. Suzuki K., Horiba I., and Sugie N.: Efficient approximation of a neural filter for quantum noise removal in x-ray images. Proc. IEEE Int. Workshop on Neural Networks for Signal Processing (NNSP) IX: 370-379, 1999.
    77. Suzuki K., Horiba I., Sugie N., and Nanki M.: Noise reduction of medical x-ray image sequences using a neural filter with spatiotemporal inputs. Proc. Int. Symp. Noise Reduction for Imaging and Communication Systems (ISNIC) pp. 85-90, 1998.
    78. Suzuki K., Horiba I., Sugie N., and Nanki M.: A recurrent neural filter for reducing noise in medical x-ray image sequences. Proc. Int. Conf. Neural Information Processing (ICONIP) 1: 157-160, 1998.
    79. Suzuki K., Horiba I., and Sugie N.: Designing the optimal structure of a neural filter. Proc. IEEE Int. Workshop on Neural Networks for Signal Processing (NNSP) VIII: 323-332, 1998.
    80. Suzuki K., Horiba I., Sugie N., and Ikeda S.: Improvement of image quality of x-ray fluoroscopy using spatiotemporal neural filter which learns noise reduction, edge enhancement and motion compensation. Proc. Int. Conf. Signal Processing Applications and Technology (ICSPAT) 2: 1382-1386, 1996.

In Japanese

    1. Inaba T., He L., Murakami K., Suzuki K.: A study on temporal subtraction of chest radiographs using a genetic algorithm. Proc. Joint Conf. of Institutes of Electronics-Related Engineers p. P-059, 2008.
    2. Ozawa Y., He L., Murakami K., Suzuki K.: A study on rib suppression in chest radiographs. Proc. Joint Conf. of Institutes of Electronics-Related Engineers p. P-065, 2008.
    3. Arimura H., Katsuragawa S., Suzuki K., Li F., Shiraishi J., Doi K., and Sone S.: Development of a CAD scheme for lung nodule detection on CT images in lung cancer screening. Proc. 32nd Annual Meeting of Japanese Society of Radiological Technology, 2004.
    4. Suzuki K., Horiba I., and Sugie N.: Edge detection from noisy images using a neural edge detector. Proc. 62nd Annual Meeting of Information Processing Society of Japan pp. 193-194, 2001.
    5. Ninagawa K., Umeyama T., Suzuki K., and Sugie K.: Separation of sound sources in the spatial domain in sound spectrogram. Seminar of Institute of Electrical Engineers of Japan pp. 36-37, 2001.
    6. Suzuki K., Horiba I., Sugie N., and Nanki M.: Contour extraction of the left ventricular cavity from digital subtraction angiograms using a neural edge detector. Technical Report of IEICE MI2000-35: 25-30, 2000.
    7. Suzuki K., Horiba I., Sugie N., and Nanki M.: Extraction of left ventricular contours which agree with cardiologists’ judgment. Proc. Joint Conf. of Institutes of Electronics-Related Engineers p. 352, 2000.
    8. Suzuki K., Horiba I., Sugie N., and Nanki M.: Contour extraction of left ventricles using a neural edge detector. Proc. Annual Meeting of Institute of Electronics, Information and Communication Engineers p. 368, 2000.
    9. Suzuki K., Horiba I., and Sugie N.: Fast algorithm for labeling of connected components in binary images. Technical Report of IEICE PRMU99-123: 157-164, 1999.
    10. Kurebayashi T., Uozumi E., Yoshida Y., Suzuki K., Horiba I., Okabayashi S., Yamamoto S., and Sugie N.: Effect of sounds on mind – analysis of the main theme of music -. Proc. Joint Conf. of Institutes of Electronics-Related Engineersp. 353, 1999.
    11. Suzuki K., Horiba I., and Sugie N.: Realization of the approximate filter of a neural filter by analysis of its functions. Proc. Joint Conf. of Institutes of Electronics-Related Engineers p. 420, 1997.
    12. Suzuki K., Hara N., Horiba I., Sugie N., and Ishikawa K.: A method for removing redundant units of supervised neural networks and its evaluation in an application to a neural filter. Technical Report of IEICE NC96-67: 71-78, 1996.
    13. Teramoto A., Hara N., Horiba I., Sugie N., and Suzuki K.: A method for locally selecting filters using a neural network. Proc. 7th Annual Meeting of Japanese Neural Network Society pp. 127-128, 1996.
    14. Suzuki K., Hara N., Horiba I., Sugie N., and Koike K.: A new method for optimizing the structure of a supervised neural network. Proc. 7th Annual Meeting of Japanese Neural Network Society pp. 106-107, 1996.
    15. Teramoto A., Horiba I., Sugie N., Hara N., and Suzuki K.: Improvement of image quality by adaptive K-nearest neighbor averaging filter. Technical Report of IEICE IE96-41: 1-8, 1996.
    16. Suzuki K., Hayashi T., Horiba I., Sugie N., and Koike K.: Improvement of image quality of x-ray fluoroscopy using a spatiotemporal neural filter which has learned noise reduction, edge enhancement and motion compensation. Technical Report of IEICE IE96-44: 25-32, 1996.
    17. Suzuki K., Horiba I., and Sugie N.: Noise reduction of x-ray fluoroscopy using spatiotemporal neural filter. Technical Report of IEICE IE96-13: 37-44, 1996.
    18. Suzuki K., Ikeda S., Suzuki K., and Imai N.: Development of an automated control system for x-ray filters. Proc. 52nd Annual Meeting of Japanese Society of Radiological Technology p. 80, 1996.
    19. Hara N., Teramoto A., Suzuki K., Horiba I., and Sugie N.: A method for pruning units in neural networks. Proc. Joint Conf. of Institutes of Electronics-Related Engineers p. 302, 1995.
    20. Nanki M., Kato M., Hori H., Haruta K., Horiba I., and Suzuki K.: Evaluation of a new subtraction technique in coronary DSA. Proc. 90th Annual Meeting of Japanese Circulation Society, 1993.
    21. Suzuki K., Horiba I., Ikegaya K., and Nanki M.: A method for reducing artifacts in cardiac DSA images. Proc. Joint Conf. of Institutes of Electronics-Related Engineers p. 351, 1992.
    22. Suzuki K., Ueda K., Horiba I., and Ikegaya K.: A neural network model for predicting analog values. Proc. Joint Conf. of Institutes of Electronics-Related Engineers p. 286, 1992.
    23. Suzuki K., Ema H., Ueda K., Yamada M., Horiba I., and Ikegaya K.: Recognition of the parking occupancy status using a neural network. Proc. Joint Conf. of Institutes of Electronics-Related Engineers p. 642, 1991.

Refereed Abstracts of International Conferences

  1. Suzuki K., Liu Y., Higaki T., Funama Y., and Awai K.: Supervised conversion of ultra-low-dose to higher-dose CT images by using pixel-based machine learning: Phantom and initial patient studies. Program of Scientific Assembly and Annual Meeting of Radiological Society of North America (RSNA), xxxx-06 ,2013
  2. Huynh H. T., Suzuki K., Karademir I., Kampalath R., and Oto A.: MRI Liver Volumetry Using 3D Geodesic Active Contour Segmentation Coupled with a Level-set Algorithm. Program of Scientific Assembly and Annual Meeting of Radiological Society of North America (RSNA), LL-PHS-TU7C, 2012.
  3. Suzuki K., Sheu I., Xu J., Yang S., and Dachman A. H.: Computer-aided Diagnosis (CADx) for Distinguishing Neoplastic from Non-neoplastic Lesions toward CT Colonography (CTC) Beyond Detection. Program of Scientific Assembly and Annual Meeting of Radiological Society of North America (RSNA), LL-GIS-TU5C, 2012.
  4. Suzuki K., Iinuma G., Miyake M., Shimada N., Hock D., and Dachman A. H.: Can CT Colonography (CTC) Assisted by Computer-aided Diagnosis (CADx) Be Used as a Diagnostic Tool Beyond Detection? Program of Scientific Assembly and Annual Meeting of Radiological Society of North America (RSNA), LL-GIE3076-TUB, 2012.
  5. Suzuki K., Chen S., Date S., Funama Y., and Awai K. Supervised Massive-Training Artificial Neural Network (MTANN) for Reduction of Radiation Dose in CT in an Ultra-Low-Dose Screening Setting. Program of Scientific Assembly and Annual Meeting of Radiological Society of North America (RSNA), LL-PHS-SU5B, 2012.
  6. Xu J. and Suzuki K.: Computer-Aided Detection (CADe) of Polyps in CT Colonography (CTC) with Maximal AUC Feature Selection Augmented by Manifold Learning. Program of Scientific Assembly and Annual Meeting of Radiological Society of North America (RSNA), SSA19-04, 2012.
  7. Suzuki K., Iinuma G., Miyake M., Shimada N., Hock D., and Dachman A. H.: Observer Performance Study: Effect of Computer-Aided Diagnosis (CADx) on the Performance of Radiologists in Distinguishing Neoplastic from Non-Neoplastic Lesions in CT Colonography (CTC). Program of Scientific Assembly and Annual Meeting of Radiological Society of North America (RSNA), SSA07-08, 2012.
  8. Kampalath R., Pu Y., Wroblewski K., Liao S., Shimada N., Penney B. C., Shih M., Chen S., Suzuki K., Chen C., and Appelbaum D. E.: Prognostic Value of Baseline Whole-Body Metabolic Tumor Burden on PET/CT in Surgical Patients with Non-Small Cell Lung Cancer. Program of Scientific Assembly and Annual Meeting of Radiological Society of North America (RSNA), LL-NMS-MO6B, p. 370, 2011.
  9. Xu J., and Suzuki K.: False-Positive Reduction in Computer-aided Detection (CADe) of Polyps in CT Colonography (CTC) with Manifold Learning. Program of Scientific Assembly and Annual Meeting of Radiological Society of North America (RSNA), LL-GIS-TU9B, p. 276, 2011.
  10. Xu J., and Suzuki K.: Computer-aided Detection (CADe) of Polyps in CT Colonography (CTC) with Maximal Partial AUC Feature Selection. Program of Scientific Assembly and Annual Meeting of Radiological Society of North America (RSNA), SSG13-09, p. 175, 2011.
  11. Chen S., Suzuki K., and MacMahon H.: Suppression of Ribs and Clavicles in Chest Radiographs by Means of Multiple Anatomically-specific Massive Training ANNs Combined with Total Variation Minimization Smoothing. Program of Scientific Assembly and Annual Meeting of Radiological Society of North America (RSNA), SSA19-02, p. 143, 2011.
  12. Chen S., Suzuki K., and MacMahon H.: A computer-aided diagnostic scheme for lung nodule detection in chest radiographs by means of two stage nodule-enhancement and support vector classification. Program of Scientific Assembly and Annual Meeting of Radiological Society of North America (RSNA), p. 660, 2010.
  13. Chen S., Suzuki K., and MacMahon H.: Improved computerized detection of lung nodules in chest radiographs by means of “virtual dual-energyEradiography. Program of Scientific Assembly and Annual Meeting of Radiological Society of North America (RSNA), p. 290, 2010.
  14. Hori M., Suzuki K., Epstein M. L., and Baron R. L.: CT liver volumetry: Effects of slice thickness on volume calculationECan 3D isotropic CT data improve the accuracy? Program of Scientific Assembly and Annual Meeting of Radiological Society of North America (RSNA), p. 412, 2010.
  15. Suzuki K., Hori M., Iinuma G., Friedman A. C., and Dachman A. H.: Observer Performance Study: Effect of Computer-aided Detection (CADe) on the Performance of Expert Radiologists in Detection of “DifficultE Polyps in CT Colonography (CTC) in a Multicenter Clinical Trial. Program of Scientific Assembly and Annual Meeting of Radiological Society of North America (RSNA), p. 319, 2010.
  16. Suzuki K., Sheu I., Kawaler E., Ferraro F., Rockey D. C., and Dachman A. H., Computer-aided detection (CADe) of flat lesions in CT colonography (CTC) by means of a spinning-tangent technique. Program of Scientific Assembly and Annual Meeting of Radiological Society of North America (RSNA), p. 319, 2010.
  17. Suzuki K., Kohlbrenner R. M., Kuo J., Hori M., Oto A., and Baron R. L.: Computer-aided differentiation (CADf) between hepatocellular carcinoma and hemangioma in contrast-enhanced hepatic CT by means of machine-learning regression with 3D features on watershed-segmented volumes. Program of Scientific Assembly and Annual Meeting of Radiological Society of North America (RSNA), p. 319, 2010.
  18. Xu J., Suzuki K., Hori M., Oto A., and Baron R. L.: Computer-aided Detection of Hepatocellular Carcinoma in Multiphase Contrast-enhanced Hepatic CT. Program of Scientific Assembly and Annual Meeting of Radiological Society of North America (RSNA), p. 318, 2010.
  19. Suzuki K.: Machine leaning for medical image processing and pattern recognition. Medical Physics 37: 3396, 2010. (Invited)
  20. Pu Y., Wroblewski K., Hall A., Appelbaum D., Simon C., Suzuki K., and O’Brien-Penney B.: Prognostic value of baseline whole-body metabolic tumor burden and their response indices on PET/CT in patients with non-small cell lung cancer, 2010 World Molecular Imaging Congress, Kyoto, Japan, September 2010.
  21. Suzuki K., Kohlbrenner R., Grelewicz Z., Ng E., Hori M., and Baron R. L.: Computer-aided early detection of hepatocellular carcinoma in contrast-enhanced hepatic CT by use of watershed segmentation and morphologic and texture analysis, Program of Scientific Assembly and Annual Meeting of Radiological Society of North America (RSNA), p. 334, 2009.
  22. Suzuki K., Armato S. G., Engelmann R., Caligiuri P., and MacMahon H.: Enhanced digital chest radiography: Temporal subtraction combined with “virtual dual-energyEtechnology for improved conspicuity of growing cancers and other pathologic changes, Program of Scientific Assembly and Annual Meeting of Radiological Society of North America (RSNA), p. 433, 2009.
  23. Suzuki K., Hori M., McFarland E., Friedman A. C., Rockey D. C., and Dachman A. H.: Can CAD help improve the performance of radiologists in detection of “difficultEpolyps in CT colonography?,Program of Scientific Assembly and Annual Meeting of Radiological Society of North America (RSNA), p. 872, 2009. (Awarded Certificate of Merit Award)
  24. Grelewicz Z., Suzuki K., Kohlbrenner R., Obajuluwa A. M., Ng E., Tompkins R., Epstein M. L., Hori M., and Baron R. L.: Computer-aided diagnostic scheme for detection of hepatocellular carcinoma in contrast-enhanced hepatic CT: Preliminary results. Medical Physics 36: 2434, 2009.
  25. Suzuki K., Kohlbrenner R., Obajuluwa A. M., Epstein M. L., Garg S., Hori M., and Baron R. L.: Computer-Aided Measurement of Liver Volumes in CT by Means of Fast-Marching and Level-Set Segmentation. Medical Physics 36: 2805, 2009.
  26. Hori M, Oto A., Orrin S., Suzuki K., Baron R. L.: Diffusion-weighted MR Imaging for the diagnosis of anal fistula. Annual Meeting of American Roentgen Ray Society (ARRS), 2009.
  27. Hori M., Suzuki K., Oto A., Baron R. L.: Problems in characterizing benign versus malignant liver tumors: optimizing diagnosis and potential role for computer-aided diagnosis (CAD) of liver CT. Program of Scientific Assembly and Annual Meeting of Radiological Society of North America (RSNA), p. 839, 2008.
  28. Suzuki K., Obajuluwa A. M., Epstein M. L., Hori M., Oto A., Baron R. L.: Automated CT liver volumetrics: How and why?. Program of Scientific Assembly and Annual Meeting of Radiological Society of North America (RSNA), p. 847, 2008.
  29. Suzuki K., Sheu I., Epstein M. L., Kohlbrenner R., Obara P. R., Rockey D. C., and Dachman A. H.: Integrated CAD system for detection of flat lesions and automated volume measurement of polyps in CT colonography for prevention of perceptual and measurement errors. Program of Scientific Assembly and Annual Meeting of Radiological Society of North America (RSNA), p. 1064, 2008.
  30. Suzuki K., Armato S. G., Engelmann R., Caliguiri P., MacMahon H. M.: Enhanced digital chest radiography: Temporal subtraction and virtual dual-energy chest radiography for improved conspicuity of growing cancers and other pathologic changes. Program of Scientific Assembly and Annual Meeting of Radiological Society of North America (RSNA), p. 1064, 2008.
  31. Lostumbo A., Dachman A. H., Suzuki K., Tsai J., and Wanamaker C.: Comparison of 2D and 3D views for measurement and conspicuity of flat lesions in CT colonography. Program of Scientific Assembly and Annual Meeting of Radiological Society of North America (RSNA), p. 671, 2008.
  32. Suzuki K., Sheu I., Zhang J., Hori M., Rockey D. C., and Dachman A. H.: MTANN CAD for detection of flat lesions in CT colonography in a multicenter clinical trial. Program of Scientific Assembly and Annual Meeting of Radiological Society of North America (RSNA), p. 593-594, 2008.
  33. Suzuki K., Epstein M. L., Kuo J., Obara P. R., Rockey D. C., and Dachman A. H.: Fully automated measurement of polyp volume in CT colonography using 3D volume-growing and sub-voxel refinement techniques. Program of Scientific Assembly and Annual Meeting of Radiological Society of North America (RSNA), p. 594, 2008.
  34. Suzuki K., Zhang J., Grelewicz Z., Kuo J., Rockey D. C., and Dachman A. H.: Effect of massive-training ANNs on the performance of a CAD system on “missedEpolyps in CT colonography. Medical Physics 35: 2941, 2008.
  35. Suzuki K., Armato S. G., He L., Engelmann R., Caliguiri P., MacMahon H. M.: Usefulness of “virtual dual-energy radiography (VDER)E for improving conspicuity of nodules and other pathologic changes by means of rib suppression in standard and temporally subtracted chest radiographs. Program of Scientific Assembly and Annual Meeting of Radiological Society of North America (RSNA), p. 979, 2007.
  36. Suzuki K., Verceles J., Khankari S., Lostumbo A., Rockey D. C., and Dachman A. H.: Advanced CAD system incorporating a 3D massive-training artificial neural network (MTANN) for detection of “missedE polyps in CT colonography in a large multicenter clinical trial. Program of Scientific Assembly and Annual Meeting of Radiological Society of North America (RSNA), p. 778, 2007.
  37. Suzuki K., Verceles J., Khankari S., Lostumbo A., Rockey D. C., and Dachman A. H.: Performance of a CAD scheme incorporating a massive-training artificial neural network (MTANN) for detection of polyps in false-negative CT colonography cases in a large multicenter clinical trial. Program of Scientific Assembly and Annual Meeting of Radiological Society of North America (RSNA), p. 595, 2007.
  38. King M. T., Giger M. L, Suzuki K., Bardo D. M., Greenberg B. M., Pan X., et al.: Computerized assessment of calcified plaques in cardiac CT Images. Program of Scientific Assembly and Annual Meeting of Radiological Society of North America (RSNA), p. 405, 2007.
  39. Oda S., Awai K., Suzuki K., He L., MacMahon H., and Yamashita Y.: Detection of Pulmonary Nodules on Chest Radiographs: Effect of rib suppression by means of massive training artificial neural network (MTANN) on performance of radiologists. Program of Scientific Assembly and Annual Meeting of Radiological Society of North America (RSNA), p. 419, 2007.
  40. Muramatsu C., Li Q., Schmidt R. A., Shiraishi J., Suzuki K., Newstead G. M., and Doi K.: Investigation of similarity measures for selection of similar images for breast lesions on mammograms.Medical Physics 34: 2338, 2007.
  41. Dachman A. H., Doshi T., Rusinak D., Halvorsen R. A., Rockey D. C., Suzuki K., et al.: Causes of error in CT colonography. Radiology 238(p): 725, 2006.
  42. Suzuki K., Engelmann R., MacMahon H., and Doi K.: Virtual dual-energy radiography: improved chest radiographs by means of rib suppression based on a massive training artificial neural network (MTANN). Radiology 238(p): 932, 2006.
  43. Suzuki K., Li F., Engelmann R., MacMahon H., and Doi K.: Advanced CAD system based on 3D massive-training artificial neural network (MTANN) for detection and classification of lung nodules in CT. Radiology 238(p): 787-788, 2006. (Awarded Certificate of Merit Award)
  44. Suzuki K., Yoshida H., Nappi J., Dachman A. H.: Three-dimensional massive training artificial neural network (MTANN) in CT colonography: Applications to computer-aided detection (CAD) of polyps. Radiology 238(p): 932, 2006.
  45. Suzuki K., Li F., MacMahon H., and Doi K.: Development of a sequential combination of massive-training artificial neural networks (MTANNs) to construct a new type of computer-aided diagnostic (CAD) scheme for detection of lung cancer in CT. Radiology 238(p): 597, 2006.
  46. Suzuki K., Yoshida H., Nappi J., Armato III S. G., Dachman A. H.: Mixture of expert 3D massive-training artificial neural networks for reduction of multiple types of false positives in computer-aided detection of polyps in CT colonography.Radiology 238(p): 412-413, 2006.
  47. Suzuki K., Yoshida H., Nappi J., Armato III S. G., Dachman A. H.: Massive training artificial neural network (MTANN) to reduce false positives due to rectal tubes in computer-aided polyp detection. Medical Physics 33: 2208, 2006.
  48. Yuan Y., Giger M. L., Li H., Suzuki K., Jamieson A. R., and Sennett C.: Comparison of image segmentation algorithms on digitized mammograms and FFDM images for CAD. Medical Physics 33: 2195-2196, 2006.
  49. Muramatsu C., Li Q., Schmidt R. A., Suzuki K., Shiraishi J., Newstead G. M., and Doi K.: Determination of subjective similarity for pairs of lesions on mammograms: comparison of ranking scores in 2AFC versus absolute ratings for masses and microcalcifications. Medical Physics 33: 1996, 2006.
  50. Muramatsu C., Li Q., Schmidt R. A., Suzuki K., Newstead G. M., and Doi K.: Usefulness of similar images for distinction between benign and malignant lesions on mammograms: effect of subjective similarity determined by breast radiologists. Radiology 237(p): 850, 2005.
  51. Li F., Suzuki K., Engelmann R., Sone S., MacMahon H., and Doi K.: Computer-aided diagnosis for distinguishing benign nodules from early lung cancers on low-dose CT. Radiology 237(p): 754, 2005.
  52. Suzuki K., Li F., Engelmann R., MacMahon H., and Doi K.: Advanced CAD schemes based on massive training artificial neural network (MTANN) for detection and classification of lung nodules in thoracic CT and chest radiography. Radiology 237(p): 849, 2005.
  53. Suzuki K., Li F., MacMahon H., and Doi K.: Improved chest radiographs with rib suppression by means of massive training artificial neural network (MTANN). Radiology 237(p): 817, 2005.
  54. Suzuki K., Yoshida H., Nappi J. J., Armato S. G., and Dachman A. H.: False-positive reduction in computer-aided detection of polyps in CT colonography based on multiple massive training artificial neural networks. Radiology 237(p): 440, 2005.
  55. Suzuki K., Li F., MacMahon H., and Doi K.: Distinction between lung cancers and false-positive benign nodules on low-dose CT in screening by means of massive training artificial neural network. Radiology 237(p): 393, 2005.
  56. Suzuki K., Li F., MacMahon H., and Doi K.: Differentiation of malignant nodules from benign nodules in thoracic high-resolution CT (HRCT) by use of a massive training artificial neural network. Radiology 237(p): 481, 2005.
  57. Suzuki K., Li Q., Li F., MacMahon H., and Doi K.: Reduction of false positives in CAD scheme for detection of lung nodules on MDCT using 3D massive training artificial neural network. Radiology 237(p): 393, 2005.
  58. Muramatsu C., Li Q., Schmidt R. A., Suzuki K., Shiraishi J., Newstead G. M., and Doi K.: Investigation of various methods for determination of similarity measures for pairs of clustered microcalcifications on mammograms. Medical Physics 32: 2120, 2005.
  59. Muramatsu C., Li Q., Schmidt R. A., Suzuki K., Newstead G. M., and Doi K.: Determination of the degree of subjective similarity for pairs of clustered microcalcifications on mammograms: Preliminary observer study. Radiology 233(p): 491, 2004.
  60. Muramatsu C., Li Q., Schmidt R. A., Suzuki K., Newstead G. M., and Doi K.: Usefulness of similar images for distinction between benign and malignant lesions in mammograms: determination of similarity between pairs of mammographic lesions. Radiology 233(p): 710, 2004.
  61. Shiraishi J., Li F., Li Q., Suzuki K., MacMahon H., Doi K., et al.: Recent progress in computer-aided diagnosis (CAD) for chest radiology: Interactive demonstration of computerized schemes for lung cancer detection on low-dose CT and digital chest radiography. Radiology 233(p): 798, 2004.
  62. Shiraishi J., Suzuki K., Li Q., Engelmann R., Katsuragawa S., and Doi K.: Computer-aided detection of lung nodules on chest radiographs: Evaluation with a large scale Image database. Radiology 233(p): 289-290, 2004.
  63. Suzuki K., Abe H., Li F., MacMahon H., and Doi K.: Separation of ribs and soft tissue in single chest radiographs by means of massive training artificial neural networks. Radiology 233(p): 291, 2004. (Awarded RSNA Research Trainee Prize)
  64. Suzuki K., Shiraishi J., Li F., Abe H., MacMahon H., and Doi K.: False-positive reduction in computerized detection of lung nodules in chest radiographs using massive training artificial neural networks for rib-suppression technique. Radiology 233(p): 291, 2004.
  65. Suzuki K., Li Q., Li F., MacMahon H., and Doi K.: Distinction between nodules and false positives in CAD scheme for lung nodule detection on multi-detector CT images by means of massive training artificial neural networks. Radiology233(p): 290-291, 2004.
  66. Suzuki K., Li F., Shiraishi J., Li Q., MacMahon H., and Doi K.: Analysis of radiologistsEresponses with CAD in the distinction between malignant and benign pulmonary nodules on high-resolution CT. Radiology 233(p): 289, 2004.
  67. Muramatsu C., Li Q., Schmidt R. A., Suzuki K., Newstead G. M., and Doi K.: Investigation of psychophysical measures in selecting similar images for clustered microcalcifications on mammograms. Medical Physics 31: 1795, 2004.
  68. Muramatsu C., Li Q., Suzuki K., Schmidt R. A., Newstead G. M., and Doi K.: Usefulness of psychophysical measures for selection of similar images for distinction between benign and malignant mass lesions on mammograms: A pilot study. Radiology 229(P): 170, 2003.
  69. Shiraishi J., Abe H., Suzuki K., Li Q., Engelmann R., and Doi K.: Development of a computerized scheme for detection of lung nodules in chest radiographs: new approach with anatomical segmentation technique. Radiology 229(P): 167, 2003.
  70. Suzuki K., Li F., Abe H., Sone S., and Doi K.: Massive training artificial neural network (MTANN): A novel image-processing tool for computer-aided diagnostic schemes in CT and chest radiographs. Radiology 229(P): 714, 2003. (Awarded Certificate of Merit Award)
  71. Suzuki K., Li Q., Li F., Sone S., and Doi K.: Computerized scheme for distinction between benign and malignant nodules in thoracic low-dose CT by use of massive training artificial neural network. Radiology 229(P): 564-565, 2003.
  72. Suzuki K., Shiraishi J., Abe H., and Doi K.: False positive reduction in computerized detection of lung nodules in chest radiographs using massive training artificial neural network. Radiology 229(P): 563, 2003.
  73. Arimura H., Katsuragawa S., Suzuki K., Li F., Shiraishi J., Sone S., and Doi K.: Evaluation of CAD scheme for lung nodule detection in low-dose CT by use of confirmed cancer database. Medical Physics 30: 1457, 2003.
  74. Armato III S. G., Suzuki K., MacMahon H., Metz C. E, Roy A., Doi K., Giger M. L., Sone S., Li F., Abe H., and Engelmann R.: CAD of pulmonary nodules in thoracic CT. Radiology 225(P): 699, 2002.
  75. Suzuki K., Armato III S. G., Li F., Sone S., and Doi K.: Multiple massive training artificial neural network for computerized detection of lung nodules in low-dose CT. Radiology 225(P): 712, 2002.
  76. Suzuki K., Armato III S. G., Li F., Sone S., and Doi K.: Computer-aided diagnostic scheme for detection of lung nodules in CT by use of massive training artificial neural network. Radiology 225(P): 533, 2002.
  77. Suzuki K., Armato III S. G., Sone S., and Doi K.: Massive training artificial neural network for reduction of false positives in computerized detection of lung nodules in low-dose CT. Medical Physics 29: 1322, 2002.


↑ Go to top

Invited/Review Articles in Journals/Magazines

  1. Suzuki K., Wang F., Shen D, Yan P.: Editorial, Machine Learning in Medical Imaging. International Journal of Biomedical Imaging 2012: Article ID 123727, 2 pages, 2012 (Invited).
  2. He L., Chao Y., Suzuki K., and Nakamura T.: A high-speed labeling algorithm for three-dimensional binary images. ImageLab: 21: 48-52, 2010 (Invited).
  3. He L., Chao Y., Suzuki K., Nakamura T., and Itoh H.: A fast two-scan connected-component labeling algorithm for binary images. ImageLab, 2008.
  4. Suzuki K.: Applications of computer-aided diagnosis by means of massive-training artificial neural networks (MTANN) to diagnosis of the thorax. Medical 39: 1202-1209, 2007.
  5. Suzuki K.: Massive training artificial neural network (MTANN): A versatile pattern-recognition technique which learns abnormal and normal opacities for computer-aided diagnosis. Innervision 19: 31-37, 2004 (Invited).
  6. Suzuki K.: Recommended readings on radiological imaging research – On artificial neural networks. Japanese Journal of Radiological Technology 60: 1095-1097, 2004.
  7. Suzuki K.: Recommended readings on neural networks. Gazo Tsushin 26: 48-50, 2003.
  8. Suzuki K. and Horiba I.: Recognition of artery stenosis using a neural network for predicting analogue values. ImageLab 6: 63-66, 1995 (Invited).

↑ Go to top

Book Editor / Journal Guest Editor

  1. Shen D., Wang F., Yan P., Suzuki K.: Guest Editors. Special issue on “Machine Learning in Medical Imaging,EComputerized Medical Imaging and Graphics, 2013.
  2. Suzuki K.: Editor. Computational Intelligence in Biomedical Imaging, Springer (New York, NY), 2013.
  3. Suzuki K.: Editor-in-Chief, Machine Learning, Publishing Services (Cheyenne, WY), 2013. (978-0-9835850-X-X)
  4. Suzuki K.: Editor. Artificial Neural Networks – Architectures and Applications, 256 pp., In-Tech (Vukovar, Croatia), 2013. (ISBN 978-953-51-0935-8)
  5. Wang F., Shen D., Yan P., Suzuki K.: Editors. Machine Learning in Medical Imaging (MLMI), Lecture Notes in Computer Science (Springer-Verlag, Berlin), vol. 7588 (Springer-Verlag, Berlin), 276 pp., 2012. (ISBN 978-3-642-35427-4)
  6. Suzuki K.: Guest Editor. Special issue on “Machine Learning for Medical Imaging 2012,” Algorithms, 2012.
  7. Yan P., Suzuki K., Wang F., Shen D.: Guest Editors. Special issue on “Machine Learning in Medical Imaging,” Machine Vision and Applications, 2012.
  8. Suzuki K.: Editor. Machine Learning in Computer-Aided Diagnosis: Medical Imaging Intelligence and Analysis, IGI Global (Hershey, PA), 524 pp., 2012. (ISBN 9781466600591)
  9. Suzuki K., Yan P., Wang F., Shen D.: Guest Editors. Special issue on “Machine Learning in Medical Imaging,” International Journal of Biomedical Imaging, 2011. (ISBN 9781466600591)
  10. Suzuki K., Wang F., Shen D, Yan P.: Editors. Machine Learning in Medical Imaging, Lecture Notes in Computer Science, 7009, Springer-Verlag (Berlin), 355 pp., 2011. (ISBN 978-3-642-24318-9)(Top 25% Most Downloaded eBooks in 2013 by Springer)
  11. Suzuki K.: Editor. Artificial Neural Networks – Industrial and Control Engineering Applications, 478 pp., In-Tech (Vukovar, Croatia), 2011. (ISBN 978-953-307-220-3)
  12. Suzuki K.: Editor. Artificial Neural Networks – Methodological Advances and Biomedical Applications, In-Tech (Vukovar, Croatia), 362 pp., 2011. (ISBN: 978-953-307-243-2)
  13. Wang F., Yan P., Suzuki K., Shen D: Editors. Machine Learning in Medical Imaging, Lecture Notes in Computer Science, 6357, Springer-Verlag (Berlin), 192 pp., 2010. (ISBN 978-3-642-15947-3)
  14. Suzuki K.: Guest Editor. Special issue on “Artificial Intelligence in Biomedical Image Analysis,” Open Artificial Intelligence Journal, 2010.
  15. Suzuki K.: Guest Editor. Special issue on “Machine Learning for Medical Imaging,” Algorithms, 2010.


↑ Go to top

Book Chapters

  1. Suzuki K.: Pixel-based machine learning in computer-aided diagnosis for lung and colon cancer. Machine Learning in Healthcare Informatics, Springer-Verlag (New York, NY), 2014 (Invited).
  2. Suzuki K.: Computer-Aided Detection of Flat Lesions in CT Colonography, Abdomen and Thoracic Imaging – An Engineering & Clinical Perspective, El-Baz A. Ed., Springer (New York, NY), 2013 (in press).
  3. Huynh H.T. and Suzuki K.: Liver segmentation in hepatic MRI by means of geodesic active contour model, Computational Intelligence in Biomedical Imaging, Suzuki K. Ed., Springer (New York, NY), 2013 (in press).
  4. Chen S. and Suzuki K.: Bone Suppression in Chest Radiographs by Means of Anatomically Specific Multiple Massive-Training ANNs Combined with Total Variation Minimization Smoothing and Consistency Processing. Computational Intelligence in Biomedical Imaging, Suzuki K. Ed., Springer (New York, NY), 2013 (in press).
  5. Suzuki K.: Computer-aided detection of lung nodules in chest radiographs and thoracic CT. Pulmonary Image Analysis, American Scientific (Valencia, CA), 2013 (Invited) (in press).
  6. Suzuki K.: Classification of Lesions by Use of Massive-Training Artificial Neural Networks. Handbook of Medical Image Analysis, Fujita H., Ishida T., and Katsuragawa S. Eds., Ohm (Tokyo, Japan), 2012 898 pp., 2012 (Invited). (ISBN 978-4-274-21282-6) (Invited).
  7. Suzuki K.: Neural Networks. Handbook of Medical Image Analysis, Fujita H., Ishida T., and Katsuragawa S. Eds., Ohm (Tokyo, Japan), 898 pp., 2012 (ISBN 978-4-274-21282-6) (Invited).
  8. Chen S. and Suzuki K.: Computerized detection of lung nodules on chest radiographs: Application of bone suppression imaging by means of anatomical-segment-specific multiple massive-training ANNs, Machine Learning in Computer-Aided Diagnosis: Medical Imaging Intelligence and Analysis, Suzuki K. Ed., IGI Global (Hershey, PA), pp. 122-144, 2012 (ISBN 9781466600591).
  9. Xu J. and Suzuki K.: Computer-aided detection of polyps in ct colonography by means of feature subset selection and massive-training support vector regression, Machine Learning in Computer-Aided Diagnosis: Medical Imaging Intelligence and Analysis, Suzuki K. Ed., IGI Global (Hershey, PA), pp. 178-201, 2012 (ISBN 9781466600591).
  10. Suzuki K.: Computerized segmentation of organs by means of geodesic active contour level-set algorithm. State of the Art in Image Segmentation and Registration, El-Baz A. Ed., Springer (New York, NY), pp. 103-128, 2011 (ISBN 978-1-4419-8194-3) (Invited).
  11. Suzuki K.: Massive-training artificial neural networks for supervised enhancement/suppression of lesions/patterns in medical images. Focus on Artificial Neural Networks, Nova Science Publishers (Hauppauge, NY), pp. 129-150, 2011 (ISBN 978-1-61324-285-8) (Invited).
  12. Suzuki K.: Pixel-based artificial neural networks in computer-aided diagnosis. Artificial Neural Networks – Methodological Advances and Biomedical Applications, K. Suzuki Ed., In-Tech (Vukovar, Croatia), pp. 71-92, 2011 (ISBN 978-953-307-243-2) (Invited).
  13. Suzuki K., and Dachman A. H.: Computer-aided diagnosis in CT colonography. Atlas of Virtual Colonoscopy, 2nd Edition, Dachman A. H. and Laghi A. Eds., Springer (New York), pp. 163-182, 2011 (ISBN 978-1-4419-5851-8) (Invited).
  14. Suzuki K., and Dachman A. H.: Usefulness of computer-aided diagnosis in CT colonography. Colonoscopia virtual, Patricia Carrascosa, Carlos Capunay, and Jorge A. Soto Eds., Liberia Akadia Editorial (Buenos Aires, Argentina), pp. 73-88, 2011 (ISBN 978-987-570-147-2) (Invited).
  15. Epstein M. L., Sheu I., Suzuki K.: Hessian matrix-based shape extraction and volume growing for 3D polyp segmentation in CT colonography. Pattern Recognition, Recent Advances, Adam Herout Ed., In-Tech (Vukovar, Croatia), pp. 405-418, 2010 (ISBN 978-953-7619-90-9) (Invited).
  16. Suzuki K.: Massive-training artificial neural networks (MTANN) in computer-aided detection of colorectal polyps and lung nodules in CT. Machine Learning, Yagang Zhang Ed., In-Tech (Vukovar, Croatia), pp. 343-366, 2010 (ISBN 978-953-307-033-9) (Invited).
  17. Giger M. L., and Suzuki K.: Computer-aided diagnosis (CAD). Biomedical Information Technology, David Dagan Feng Ed., Academic Press, pp. 359-374, 2007 (ISBN 978-0-12-373583-6).
  18. Suzuki K.: Focus, motion, deblurring, smoothing, edge-preserving smoothing, and image restoration. Dictionary of Cognitive Science, Japanese Cognitive Science Society Ed., 1,032 pp., Kyoritsu Shuppan, Tokyo, Japan, 2002 (ISBN 978-4320094451) (Invited).
  19. Suzuki K.: Detection of Cancer Lesions from Imaging. Machine Learning in Radiation Oncology, Deasy J. et al. Eds., Springer-Verlag (Berlin, Heidelberg), 2014 (Invited)


↑ Go to top