From my research on Algorithms for Mammogram Analysis

Craig Henderson

May 2008


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.

50.   Ferrari, R. and Rangayyan, R., 2004, Automatic Identification of the Pectoral Muscle in Mammograms, IEEE Transactions on Medical Imaging 23 (2), pp. 232-245.

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.

58.   Gonzalez, R. and Woods, R., 2002, Digital Image Processing, Prentice Hall, 793 pages.

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.

61.   M. Hartswood and R. Procter, Computer-Aided Mammography: A Case Study of Error Management in a Skilled Decision-making Task

62.   Dept. Health, 2001, Breast Screening Programme, England: 2000-01, Department of Health,

63.   M.D. Heath and K.W. Bowyer, 2000, Mass Detection by Relative Image Intensity, 5th International Workshop on Digital Mammography (IWDM)

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.

68.   Highnam, R., 1992, Model-based enhancement of mammographic images, Computing Laboratory.

69.   Highnam, R. and Brady, J., 1994, Model-Based Image Enhancement, Eurographic UK.

70.   Highnam, R. and Brady, J., 1999, Mammographic Image Analysis, Kluwer Academic Publishers, Computational Imaging and Vision392 pages.

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.

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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.

86.   Laine, A. and Schuler, S., 1993, Hexagonal wavelet processing of digital mammography.

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.

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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.

95.   L. McLees, 1997, Detecting Tiny Object: New pattern recognition approach successfully tested with mammography, other applications possible

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.

99.   Mutiso, A., Datamining in Medical Applications: Computer-aided diagnosis (CAD) in medical imaging with an emphasis on mammography.

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.

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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.

110.   Penedo, M. and Pearlman, W., 2003, Region-Based Wavelet Coding Methods for Digital Mammography, IEEE Transactions on Medical Imaging 22 (10), pp. 1288-1296.

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.

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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.

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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.

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