From my research on Algorithms for Mammogram Analysis
1. 2001, Diagnostic In-Training Exam 2001
2. 2003, Patient Info: Mammography Education
3. 2003, SMF™ Technology
4. Adams, N. and Williams, C., 2003, Dynamic trees for image modelling, Image and Vision Computing 20 (10), pp. 865-877.
5. Amendolia, S., Bisogni, M., Bottigli, U., Ceccopieri, A., Delogu, P., Marchi, A., Marzulli, V., Palmiero, R. and Stumbo, S., 2000, The CALMA Project: a CAD tool in breast radiology.
6. American Cancer Society, 2004, Breast Cancer: Early Detection 2004
7. Amersham Health, 2004, The Encyclopaedia of Medical Imaging Volume III
8. Apgar, B., 1999, Is Asymmetric Breast Tissue a Sign of Malignancy, American Family Physician.
9. Aylward, S., Hemminger, B. and Pisano, E., 1998, Mixture Modelling for Digital Mammogram Display and Analysis, Digital Mammography 13 pp. 305-312.
10. Azar, F., Metaxas, D., Miller, R. and Schnall, M., 1999, Methods for Modelling and Predicting Mechanical Deformations of the Breast during Interventional Procedures, 26th International Conference on Computer Graphics and Interactive Techniques.
11. Azar, F., Metaxas, D. and Schnall, M., 1999, A Finite Element Model of the Breast for Predicting Mechanical Deformations during Interventional Procedures, 7th Scientific Meeting & Exhibition of the International Society for Magnetic Resonance in Medicine.
12. Baguia, S., Bagui, S., Palc, K. and Pald, N., 2003, Breast cancer detection using rank nearest neighbor classification rules, Pattern Recognition 36 (1), pp. 25-34.
13. Bakic, P., Brzakovic, D., Brzakovic, P. and Zhu, Z., 1998, An Approach to Using a Generalized Breast Model to Segment Digital Mammograms, Proc 11th IEEE Symp Computer-Based Medical Systems, Lubbock, TX..
14. Bakic, P. and Brzakovic, Z., 1998, Anatomic segmentation of mammograms via breast model.
15. Bansal, R., 1999, Information Theoretic Integrated Segmentation and Registration of Dual 2D Portal Images and 3D CT Images, Faculty of the Graduate School.
16. Behrenbruch, C., Mariasa, K., Armitagea, P., Yama, M., Moorec, N., English, R., Clarke, J. and Brady, J., 2003, Fusion of contrast-enhanced breast MR and mammographic imaging data, Medical Image Analysis 7 (3), pp. 311-340.
17. Blot, L. and Zwiggelaar, R., 2001, Background Texture Extraction for the Classification of Mammographic Parenchymal Patterns, Medical Image Understanding and Analysis 2001.
18. Blot, L. and Zwiggelaar, R., 2002, Using hint for Risk Assessment in Mammography, Medical Image Understanding and Analysis.
19. Board, M. and Astley, S., 2002, A Novel Method of Evaluating Feature Detection Algorithms in Medical Images, Medical Image Understanding and Analysis.
20. Boggis, C. and Astley, S., 2000, Computer-assisted mammographic imaging, Breast Cancer Research 2 (6), pp. 392-395.
21. Bovik, A., 2000, Handbook of Image Processing, Communications, Networking, and Multimedia-891.
22. Bovis, K. and Singh, S., 2002, Classification of Mammographic Breast Density Using a Combiner Classifier Paradigm, Medical Image Understanding and Analysis (MIUA) Conference.
23. Bovis, K. and Singh, S., 2000, Detection of Masses in Mammograms Using Texture Features, Proc.15th International Conference on Pattern Recognition, Barcelona, IEEE Press 2 pp. 267-270.
24. Bovis, K. and Singh, S., 2002, Learning the Optimal Contrast Enhancement of Mammographic Breast Masses, 6th International Workshop on Digital Mammography.
25. Bovis, K., Singh, S., Fieldsend, J. and Pinder, C., 2000, Identification of masses in digital mammograms with MLP and RBF Nets, Proc. IEEE International Joint Conference on Neural Networks 1 pp. 342-347.
26. Breast Cancer Society, 2004, Breast Cancer History
27. Brown, M., Wilson, L., Doust, B., Dill, R. and Sun, C., 1998, Knowledge-based method for segmentation and analysis of lung boundaries in chest X-ray images, Computerized Medical Imaging and Graphics 22 (998), pp. 463-477.
28. Butt, M. and Maragos, P., 1998, Optimum Design of Chamfer Distance Transforms, IEEE Transactions on Image Processing 7 (10), pp. 1477-1484.
29. Cachier, P. and Rey, D., 2000, Symmetrization of the Non-Rigid Registration Problem using Inversion-Invariant Energies: Application to Multiple Sclerosis, Medical Image Computing and Computer-Assisted Interventionpp. 472-481.
30. Caleb, P., Classification of Surface defects on Hot Rolled Steel Using Adaptive Learning Methods.
31. Cardenosa, G., 2001, Breast Imaging Companion, Lippincott Williams & Wilkins, 495 pages.
32. Chakraborty, A., 1996, Feature and Module Integration for Image Segmentation, Yale University, Faculty of the Graduate School.
33. Chandrasekhar, R. and Attikiouzel, Y., 1999, Digitization Regime as a Cause for Variation in Algorithm Performance Across Two Mammogram Databases, Fourth International Workshop on Digital Mammography.
34. Chang, Y., Wang, X., Hardesty, L., Chang, T., Poller, W., Good, W. and Gur, D., 2002, Computerized Assessment of Tissue Composition on Digitized Mammograms, Academic Radiology 9 (8), pp. 899-905.
35. Chelberg, D., Hsu, J., Babbs, F., Zygmunt, P. and Delp, E., 1994, Digital Stereomammography, Proceedings of the 2nd International Workshop on Digital Mammography(July), pp. 181-190.
36. Cheng, H., Wang, J. and Shi, X., 2004, Microcalcification detection using fuzzy logic and scale space approaches, Pattern Recognition 37 (2), pp. 363-375.
37. Cheng, H., Xue, M. and Shi, X., 2003, Contrast enhancement based on a novel homogeneity measurement, Pattern Recognition 36 (11), pp. 2687-2697.
38. Christoyanni, I., Dermatas, E. and Kikkinakis, G., 2001, Neural Classification of Abnormal Tissue in Digital Mammography using Statistical Features of the Texture, International Conference on Neural Networks and Expert Systems in Medicine and Healthcare (NNESMED'01).
39. Ciatto, S., Visioli, C., Paci, E. and Zappa, M., 2004, Breast density as a determinant of interval cancer at mammographic screening, British Journal of Cancer 90 .
40. A. Clark, 2003, The Mammographic Image Analysis Society MiniMammography Database
41. M.L. Comer, S. Liu and E.J. Delp, 1996, Statistical Segmentation of Mammograms, Proceedings of the 3nd International Workshop on Digital Mammography478 pages
42. S.C. Dakin and R.J. Watt, 1994, Detection of bilateral symmetry using spatial filters
43. Davies, D., 1993, Technical note: digital mammography--the comparative evaluation of film digitizers, British Journal of Radiology 66 (790), pp. 930-933.
44. Day, N. and Warren, R., 2000, Mammographic screening and mammographic patterns, Breast Cancer Research 2 (6), pp. 247-251.
45. Degenhard, A., Tanner, C., Hayes, C., Hawkes, D. and Leach, M., 2001, Differentiating benign and malignant breast lesions using half Fourier MR image acquisition techniques, Medical Image Understanding and Analysis 2001.
46. Department of Health, 2003, Breast Screening Programme
47. Department of Health, 2002, Breast Screening Programme, England: 2001-02, Department of Health,
48. Distasi, R., Nappi, M. and Vitulano, S., 2002, HEAT: Hierarchical Entropy Approach for Texture Indexing in Image Databases, International Journal of Software Engineering and Knowledge Engineering 12 (5), pp. 501-521.
49. Elmore, J.G. and Carney, P.A., 2004, Computer-Aided Detection of Breast Cancer: Has Promise Outstripped Performance?, Journal of the National Cancer Institute 96 (3), pp. 162-163.
51. Ferrari, R., Rangayyan, R., Desautels, J. and Frere, A., 2001, Analysis of asymmetry in mammograms via directional filtering with Gabor wavelets, IEEE Transactions on Medical Imaging 20 (9), pp. 953-964.
52. Franceschini, M., Moesta, K., Fantini, S., Gaida, G., Gratton, E., Jess, H., Mantulin, W., Seeber, M. and Schlag, P., Frequency-domain techniques enhance optical mammography: Initial clinical results.
53. Furukawa, Y. and Shinagawa, Y., 2003, Accurate and robust line segment extraction by analyzing distribution around peaks in Hough space, Computer Vision and Image Understanding 92 (1), pp. 1-25.
54. D. Gavaghan, 1991, Differential Compression Mammography
55. F. Georgsson, 2003, Computer Aided Mammographic Screening
56. Georgsson, F., 2003, Differential Analysis of Bilateral Mammograms, International Journal of Pattern Recognition and Artificial Intelligence 17 (7), pp. 1207-1226.
57. Georgsson, F., 2003, Anatomical Coordinate System for Bilateral Registration of Mammograms, Lecture Notes in Computer Science 2749 (1), pp. 335-342.
59. R. Gupta and P.E. Undrill, The use of texture analysis to identify suspicious masses in mammography
60. Gur, D., Sumkin, J., Rockette, H., Ganott, M., Hakim, C., Hardesty, L., Poller, W., Shah, R. and Wallace, L., 2004, Changes in Breast Cancer Detection and Mammography Recall Rates After the Introduction of a Computer-Aided Detection System, Journal of the National Cancer Institute 96 (3), pp. 185-190.
62. Dept. Health, 2001, Breast Screening Programme, England: 2000-01, Department of Health,
64. Heath, M., Bowyer, K., Kopans, D., Kegelmeyer, P., Moore, R., Chang, K. and Munishkumaran, S., 1998, Current status of the Digital Database for Screening Mammography, Digital Mammography pp. 457-460.
65. M.D. Heath, K.W. Bowyer, D. Kopans, R. Moore and P. Kegelmeyer, Jr., 2003, The Digital Database for Screening Mammography 2003
66. Heine, J. and Malhotra, P., 2002, Mammographic Tissue, Breast Cancer Risk, Serial Image Analysis, and Digital Mammography. Part 1: Tissue and Related Risk Factors, Academic Radiology 9 (3), pp. 298-316.
67. Heine, J. and Malhotra, P., 2002, Mammographic Tissue, Breast Cancer Risk, Serial Image Analysis, and Digital Mammography. Part 2. Serial Breast Tissue Change and Related Temporal Influences, Academic Radiology 9 (3), pp. 317-335.
69. Highnam, R. and Brady, J., 1994, Model-Based Image Enhancement, Eurographic UK.
71. Highnam, R., Brady, J. and Shepstone, B., 1998, Estimation of Compressed Breast Thickness During Mammography, The British Journal of Radiology 71 pp. 646-653.
72. Highnam, R., Brady, J. and Shepstone, B., 1996, A Representation for Mammographic Image Processing, Medical Image Analysis 1 (1), pp. 1-18.
73. Highnam, R., Brady, J. and Shepstone, B., 1997, The hint representation and calcifications, Proceedings of Medical Image Understanding and Analysis.
74. Highnam, R., Brady, J. and Shepstone, B., 1997, Mammographic image analysis, European Journal of Radiology(24), pp. 20-32.
75. Highnam, R., Brady, J. and Shepstone, B., 1994, Computing the scatter component of mammographic images, IEEE Transactions on Medical Imaging 13 (2), pp. 301-313.
76. Highnam, R., Brady, J. and Shepstone, B., 1996, Removing the anti-scatter grid in mammography, 3rd International Workshop on Digital Mammography.
77. Highnam, R., Kita, Y., Brady, J., Shepstone, B. and English, B., 1998, Determining Correspondence Between Views, 4th International Workshop on Digital Mammography (IWDM).
78. Hsu, J., Babbs, F., Chelberg, D., Pizlo, Z. and Delp, E., 1993, A Study of the Effectiveness of Stereo Imaging with Applications in Mammography.
79. Hsu, J., Chelberg, D., Babbs, F., Zygmunt, P. and Delp, E., 1995, Pre-Clinical ROC Studies of Digital Stereomammography, IEEE Transactions on Medical Imageing 14 (2), pp. 318-327.
80. Karssemeijer, N., 1998, Automated classification of parenchymal patterns in mammograms, Physics in Medicine and Biology 43 (2), pp. 365-378.
82. Kim, K., Park, J., Song, M.K., I.C. and Suen, C., 2004, Detection of ridges and ravines using fuzzy logic operations, Pattern Recognition Letters 25 (6), pp. 743-751.
83. Kita, Y., Highnam, R. and Brady, J., 2001, Correspondence between Different View Breast X Rays Using Curved Epipolar Lines, Computer Vision and Image Understanding 83 (1), pp. 38-56.
84. Kok-Wiles, S., Brady, J. and Highnam, R., 1998, Comparing Mammogram Pairs for the Detection of Lesions, 4th International Workshop on Digital Mammography (IWDM).
85. Laine, A., Fan, J. and Yang, W., 1995, Wavelets for Contrast Enhancement of Digital Mammography, IEEE Engineering in Medicine and Biology Magazine 14 (5), pp. 536-550.
87. Laine, A., Schuler, S., Fan, J. and Huda, W., 1994, Mammographic Feature Enhancement by Multiscale Analysis, IEEE Transactions on Medical Imaging 13 (4), pp. 725-752.
88. Leavers, V., 1993, Survey: Which Hough Transform?, Computer Vision, Graphics and Image Processing: Image Understanding 58 (2), pp. 250-264.
89. Marias, K., Behrenbruch, C., Highnam, R., Brady, J., Parbhoo, S. and Seifalian, A., 2001, Volume preserving elastic transformation for local breast-tissue quantification, Proceedings Medical Image Understanding and Analysispp. pp113-116.
90. Marti, R., Zwiggelaar, R. and Rubin, C., 2001, Tracking Mammographic Structures Over Time, The British Machine Vision Conference.
91. Marti, R., Zwiggelaar, R. and Rubin, C., 2001, Automatic Mammographic Registration: Towards the Detection of Abnormalities, Medical Image Understanding and Analysis.
92. Masek, M., 2004, Hierarchical Segmentation of Mammograms Based on Pixel Intensity, School of Electrical, Electronic & Computer Engineering.
93. Masek, M., deSilva, C. and Attikiouzel, Y., 2002, Automatic Breast Orientation in Mediolateral Oblique View Mammograms, 6th International Workshop on Digital Mammography.
94. McClatchey, R., Manset, D., Hauer, T., Estrella, F., Saiz, P., Rogulin, D. and Buncic, P., 2003, The MammoGrid Project Grids Architecture, Computing in High Energy and Nuclear Physics 2003 Conference.
96. Mendez, A., Souto, M., Tahoces, P. and Vidal, J., 2003, Computer aided diagnosis for breast masses detection on a telemammography system, Computerized Medical Imaging and Graphics 27 (6), pp. 497-502.
97. Mendez, A., Tahoces, P., Lado, M., Souto, M., Correa, J. and Vidal, J., 1996, Automatic detection of breast border and nipple in digital mammograms, Computer Methods and Programs in Biomedicine 49 (3), pp. 253-262.
98. Miller, P. and Astley, S., 1993, Automated detection of mammographic asymmetry using anatomical features, International Journal of Pattern Recognition and Artificial Intelligence 7 (6), pp. 1461-1476.
100. Nagy, G. and Xu, Y., 1997, Bayesian subsequence matching and segmentation, Pattern Recognition Letters 18 (11-13), pp. 1117-1124.
101. Noyer, J.-C. , Lanvin, P. and Benjelloun, M., 2004, Non-linear matched filtering for object detection and tracking, Pattern Recognition Letters 25 (6), pp. 655-668.
102. O'Doherty, T., Review of the Effective Image Processing Techniques of Mammograms.
103. Ojala, T., Nappia, J. and Nevalainen, O., 2001, Accurate segmentation of the breast region from digitized mammograms, Computerized Medical Imaging and Graphics 25 (1), pp. 47-59.
104. Olsén, C. and Georgsson, F., 2003, The Accuracy of Geometric Approximation of the Mamilla in Mammograms, Computer Assisted Radiology and Surgery, Excerpta Medica ICS 1256pp. 956-961.
105. Open-Source, 2003, Boost.
107. Papadopoulos, A., Fotiadis, D. and Likas, A., 2002, An automatic microcalcification detection system based on a hybrid neural network classifier, Artificial Intelligence in Medicine 25 (2), pp. 149-167.
108. Parr, T., Taylor, C., Astley, S. and Boggis, C., 1997, Statistical Modelling of Oriented Line Patterns in Mammograms, Proceedings of SPIE 3034, Medical Imaging 97, 22-28 February, Newport Beach, California.
109. Parr, T., Zwiggelaar, R., Taylor, C., Astley, S. and Boggis, C., 1997, The Detection of Stellate Lesions in Digital Mammography, Proceedings of Medical Image Understanding and Analysis `97pp. 67-72.
111. Petroudi, S., Kadir, T. and Brady, J., 2003, Automatic Classification of Mammographic Parenchymal Patterns: A Statistical Approach, IEEE Engineering in Medicine and Biology Society.
112. Petroudi, S., Marias, K., English, R., Adams, R. and Brady, J., 2002, Classification of mammogram patterns using area measurements and the Standard Mammogram Form, Medical Image Understanding and Analysis Conference.
113. Piccoli, C., Feig, S. and Palazzo, J., 1999, Developing Asymmetric Breast Tissue, Radiology 211 pp. 111-117.
114. Qi, H. and Head, J., 2001, Asymmetry analysis using automatic segmentation and classification for breast cancer detection in thermograms, IEEE Engineering in Medicine and Biology Society Annual Conference (EMBS), Turkey.
115. Qi, H., Snyder, W., Head, J. and Elliott, R., 2000, Detecting breast cancer from infrared images by asymmetry analysis, Proceedings of the 22nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Chicago, MI.
116. Richard, F.J.P., 2003, The design of a markovian image matching technique and its comparison to variational technique in the context of mammogram registration, The first IAPR-TC3 conferences on Artificial Neural Networks on Pattern Recognitionpp. 82-88.
117. Richard, F.J.P. and Cohen, L.D., 2003, A new Image Registration technique with free boundary constraints: application to mammography, Computer Vision and Image Understanding 89 pp. 166-196.
118. Richard, F.J.P. and Graffigne, C., 2000, An image-matching model for the registration of temporal or bilateral mammogram pairs, 5th International Workshop on Digital Mammography (IWDM) pp. 756-762.
119. Roberts, L.M., Kahn, C.E. and Haddaway, P., 1995, Development of a Bayesian Network for Diagnosis of Breast Cancer, International Joint Conference on Artificial Intelligence (IJCAI-95) Workshop on Building Probabilistic Networks, Montréal, Québec, Canada.
120. Rueckert, D., Sonoda, L.I., Hayes, C., Hill, D., Leach, M.O. and Hawkes, D., 1999, Non-rigid Registration using Free-Form Deformations: Application to Breast MR Images, IEEE Transactions on Medical Imaging 18 (8), pp. 712-721.
121. D. Rueckert, L. Sonoda and D. Hawkes, 2003, Non-Rigid Registration of Breast MR Images using Mutual Information
122. Saha, P., Udupa, J., Conant, E., Chakraborty, D. and Sullivan, D., 2001, Breast Tissue Density Quantification Via Digitized Mammograms, IEEE Transactions on Medical Imaging 20 (8), pp. 792-803.
123. Sajda, P., Spence, C. and Parra, L., 2003, A multi-scale probabilistic network model for detection, synthesis and compression in mammographic image analysis, Medical Image Analysis 7 (2), pp. 187-204.
124. Salfity, M., Kaufmann, G., Granitto, P. and Ceccatto, H., A Computer-Aided Diagnosis Method for Automated Detection and Classification of Clustered Microcalcifications in Mammograms.
125. Sallam, M. and Bowyer, K., 1999, Registration and difference analysis of corresponding mammogram images, Medical Image Analysis 3 (2), pp. 103-118.
126. Samuels, T., 1998, Breast Imaging, Postgraduate Medicine 104 (5), .
127. Sato, J. and Cipolla, R., 1995, Image registration using multi-scale texture moments, Image and Vision Computing 13 (5), pp. 341-353.
128. Schnabel, J., Tanner, C., Castellano Smith, A.D., Leach, M.O., Hayes, C., Degenhard, A., Hose, R., Hill, D. and Hawkes, D., 2001, Validation of Non-Rigid Registration using Finite Element Methods.
129. Schnabel, J., Tanner, C., Castellano Smith, A., Degenhard, A., Hayes, C., Leach, M., Hose, D., Hill, D. and Hawkes, D., 2002, Validation of Non-Rigid Registration of Contrast-Enhanced MR Mammography using Finite Element Methods, Bildverarbeitung für die Medizin, Informatik Aktuellpp. 143-146.
130. J. Sciarretta and A. Samani, 2000, Finite Element Modeling of Soft Tissues
131. Scutt, D., Manning, J., Whitehouse, G., Leinster, S. and Massey, C., 1997, The relationship between breast asymmetry, breast size and the occurrence of breast cancer, The British Journal of Radiology 70 (838), pp. 1017-1021.
132. Sersic, D. and Loncaric, S., Enhancement of Mammographic Images for Detection of Microcalcifications.
134. Sivaramakrishna, R., Obuchowski, N., Chilcote, W. and Powell,K.A., 2001, Automatic Segmentation of Mammographic Density, Academic Radiology 8 (3), pp. 250-256.
135. Smits, P. and Dellepiane, S., 1997, An irregular MRF region label model for multi-channel image segmentation, Pattern Recognition Letters 18 (11-13), pp. 1133-1142.
138. Suckling, J., Parker, J., Dance, D., Astley, S., Hutt, I., Boggis, C.R.M., Ricketts, I., Stamatakis, E., Cerneaz, N., Kok, S., Taylor, P., Betal, D. and Savage, J., 1994, The Mammographic Image Analysis Society digital mammogram database, Exerpta Medica, International Congress Series 1069 pp. 375-378.
139. Tanner, C., Degenhard, A., Schnabel, J., Castellano Smith, A., Hayes, C., Sonoda, L., Leach, M., Hose, D., Hill, D. and Hawkes, D., 2001, A Method for the Comparison of Biomechanical Breast Models, IEEE Workshop on Mathematical Methods in Biomedical Image Analysis (MMBIA 2001)pp. 11-18.
140. Torres, R. and F.J., R., 2002, Hough transform for robust segmentation of underwater multispectral images, Center for Subsurface Sensing and Imaging Systems (CENSSIS) Research and Industrial Collaboration Conference.
141. Ulrich, M., Steger, C. and Baumgartner, A., 2003, Real-time object recognition using a modified generalized Hough transform, Pattern Recognition 36 (11), pp. 2557-2570.
142. Vincent, L., 1993, Morphological Grayscale Reconstruction in Image Analysis: Applications and Efficient Algorithms, IEEE Transactions on Image Processing 2 (2), pp. 176-201.
143. Vitulano, S., Ruberto, C. and Nappi, M., 1997, Different methods to segment biomedical images, Pattern Recognition Letters 18 (11-13), pp. 1125-1131.
144. Vujovic, N. and Brzakovic, D., 1997, Establishing the Correspondence Between Control Points in Pairs of Mammographic Images, IEEE Trans. Image Processing 6 (10), pp. 1388-1399.
145. Wirth, M., 1999, A Non-Rigid Approach to Medical Image Registration: Matching Images of the Breast, Department of Computer Systems Engineering, Faculty of Engineering.
146. Wirth, M., Choi, C. and Jennings, A., 1999, A Nonrigid-Body Approach To Matching Mammograms, IEE Image Processing and its Applicationspp. 484-487.
147. Wirth, M., Narhan, J. and Gray, D., 2002, Non rigid mammogram registration using mutual information, Proceedings of SPIE Medical Imaging: Image Processing 4684 pp. 562-573.
149. Wirth, M. and Stapinski, A., 2003, Segmentation of the breast region in mammograms using active contours, Visual Communications and Image Processing 5150 pp. 1995-2006.
150. Wirth, M. and Stapinski, A., 2003, Using active contours to extract the breast region in mammograms.
151. Wirth, M., Wang, R. and Lyon, J., 2003, Segmentation of high-intensity artifacts from mammograms using morphological reconstruction.
152. Wirth, M., Wang, R. and Stapinski, A., 2003, An algorithm for extracting the skin surface in MR breast images, 11th Scientific Meeting of the International Society for Magnetic Resonance in Medicinepp. 947.
155. Yin, F., Giger, M., Vyborny, C., Doi, K. and Schmidt, R., 1993, Comparison of bilateral-subtraction and single-image processing techniques in the computerised detection of mammographic masses, Investigative Radiology 28 (6), pp. 473-481.
157. Zacharakis, G., Zolindaki, A., Sakkalis, V., Filippidis, G., Koumantakis, E. and Papazogloub, T.G., 1999, Nonparametric characterization of human breast tissue by the Laguerre expansion of the kernels technique applied on propagating femtosecond laser pulses through biopsy samples, Applied Physics Letters 74 (5), pp. 771-772.
158. Zheng, B., Ganott, M.A., Britton, C.A., Hakim, C.M., Hardesty, L., Chang, T., Rockette, H.E. and Gur, D., 2001, Soft-copy mammographic readings with different computer assisted detection environments: Preliminary findings, Radiology 221 pp. 633-640.
159. Zlochin, M. and Dorigo, M., 2000, Model-based search for combinatorial optimization: A comparative study.
160. Zwiggelaar, R., 1999, Separating Background Texture and Image Structure in Mammograms.
161. Zwiggelaar, R. and Boggis, C.R.M., 2001, Classification of Linear Structures in Mammographic Images, Medical Image Understanding and Analysis.
162. Zwiggelaar, R. and Boggis, C.R.M., 2002, The Benefit of Knowing Your Linear Structures in Mammographic Images, Medical Image Understanding and Analysis.
163. Zwiggelaar, R., Parr, T.C. and Taylor, C.J., 1996, Finding Orientated Line Patterns in Digital Mammographic Images, Proceedings of the 7th British Machine Vision Conference pp. 715-724.