Journal of Digital Imaging

, Volume 23, Issue 5, pp 611–631 | Cite as

Computer-Aided Detection of Architectural Distortion in Prior Mammograms of Interval Cancer

  • Rangaraj M. RangayyanEmail author
  • Shantanu Banik
  • J. E. Leo Desautels


Architectural distortion is an important sign of breast cancer, but because of its subtlety, it is a common cause of false-negative findings on screening mammograms. This paper presents methods for the detection of architectural distortion in mammograms of interval cancer cases taken prior to the detection of breast cancer using Gabor filters, phase portrait analysis, fractal analysis, and texture analysis. The methods were used to detect initial candidates for sites of architectural distortion in prior mammograms of interval cancer and also normal control cases. A total of 4,224 regions of interest (ROIs) were automatically obtained from 106 prior mammograms of 56 interval cancer cases, including 301 ROIs related to architectural distortion, and from 52 prior mammograms of 13 normal cases. For each ROI, the fractal dimension and Haralick’s texture features were computed. Feature selection was performed separately using stepwise logistic regression and stepwise regression. The best results achieved, in terms of the area under the receiver operating characteristics curve, with the features selected by stepwise logistic regression are 0.76 with the Bayesian classifier, 0.73 with Fisher linear discriminant analysis, 0.77 with an artificial neural network based on radial basis functions, and 0.77 with a support vector machine. Analysis of the performance of the methods with free-response receiver operating characteristics indicated a sensitivity of 0.80 at 7.6 false positives per image. The methods have good potential in detecting architectural distortion in mammograms of interval cancer cases.

Key words

Breast diseases computer-assisted detection computer-aided diagnosis (CAD) digital image processing image analysis mammography CAD pattern recognition ROC-based analysis 



This project was funded by grants from the Canadian Breast Cancer Foundation: Prairies/NWT Chapter, the Alberta Heritage Foundation for Medical Research (AHFMR), and the Natural Sciences and Engineering Research Council (NSERC) of Canada.


  1. 1.
    Jemal A, Clegg LX, Ward E, Ries LAG, Wu X, Jamison PM, Wingo PA, Howe HL, Anderson RN, Edwards BK: Annual report to the nation on the status of cancer, 1975–2001, with a special feature regarding survival. Cancer 101(1):3–27, 2004CrossRefPubMedGoogle Scholar
  2. 2.
    Blanks RG, Wallis MG, Moss SM: A comparison of cancer detection rates achieved by breast cancer screening programmes by number of readers, for one and two view mammography: Results from the UK National Health Service Breast Screening Programme. J Med Screen 5(4):195–201, 1998PubMedGoogle Scholar
  3. 3.
    Doi K: Computer-aided diagnosis in medical imaging: Historical review, current status and future potential. Comput Med Imaging Graph 31:198–211, 2007CrossRefPubMedGoogle Scholar
  4. 4.
    Doi K: Diagnostic imaging over the last 50 years: Research and development in medical imaging science and technology. Phys Med Biol 51:R5–R27, 2006CrossRefPubMedGoogle Scholar
  5. 5.
    Homer MJ: Mammographic Interpretation: A Practical Approach, 2nd edition. New York: McGraw-Hill, 1997Google Scholar
  6. 6.
    Knutzen AM, Gisvold JJ: Likelihood of malignant disease for various categories of mammographically detected, nonpalpable breast lesions. Mayo Clin Proc 68:454–460, 1993PubMedGoogle Scholar
  7. 7.
    Matsubara T, Ichikawa T, Hara T, Fujita H, Kasai S, Endo T, Iwase T: Novel method for detecting mammographic architectural distortion based on concentration of mammary gland. Int Congr Ser 1268:867–871, 2004CrossRefGoogle Scholar
  8. 8.
    Baker JA, Rosen EL, Lo JY, Gimenez EI, Walsh R, Soo MS: Computer-aided detection (CAD) in screening mammography: Sensitivity of commercial CAD systems for detecting architectural distortion. Am J Roentgenol 181:1083–1088, 2003Google Scholar
  9. 9.
    Broeders MJM, Onland-Moret NC, Rijken HJTM, Hendriks JHCL, Verbeek ALM, Holland R: Use of previous screening mammograms to identify features indicating cases that would have a possible gain in prognosis following earlier detection. Eur J Cancer 39:1770–1775, 2003CrossRefPubMedGoogle Scholar
  10. 10.
    Rangayyan RM, Ayres FJ: Gabor filters and phase portraits for the detection of architectural distortion in mammograms. Med Biol Eng Comput 44:883–894, 2006CrossRefPubMedGoogle Scholar
  11. 11.
    Ayres FJ, Rangayyan RM: Detection of architectural distortion in mammograms via analysis of phase portraits and curvilinear structures. In: Hozman J, Kneppo P Eds. Proceedings of EMBEC’05: 3rd European Medical & Biological Engineering Conference, volume 11. Prague, Czech Republic, November 2005, pp 1768–1773Google Scholar
  12. 12.
    Ayres FJ, Rangayyan RM: Reduction of false positives in the detection of architectural distortion in mammograms by using a geometrically constrained phase portrait model. Int J Comput Assist Radiol Surg 1:361–369, 2007CrossRefGoogle Scholar
  13. 13.
    Rangayyan RM, Ayres FJ, Desautels JEL: A review of computer-aided diagnosis of breast cancer: Toward the detection of subtle signs. J Franklin Inst 344:312–348, 2007CrossRefGoogle Scholar
  14. 14.
    Tourassi GD, Delong DM, Floyd Jr, CE: A study on the computerized fractal analysis of architectural distortion in screening mammograms. Phys Med Biol 51(5):1299–1312, 2006CrossRefPubMedGoogle Scholar
  15. 15.
    Sampat MP, Markey MK, Bovik AC: Measurement and detection of spiculated lesions. IEEE Southwest Symposium on Image Analysis and Interpretation. IEEE Computer Society, March 2006, pp 105–109Google Scholar
  16. 16.
    Rangayyan RM, Prajna S, Ayres FJ, Desautels JEL: Detection of architectural distortion in mammograms acquired prior to the detection of breast cancer using Gabor filters, phase portraits, fractal dimension, and texture analysis. Int J Comput Assist Radiol Surg 2(6):347–361, 2008CrossRefGoogle Scholar
  17. 17.
    Guo Q, Shao J, Ruiz VF: Characterization and classification of tumor lesions using computerized fractal-based texture analysis and support vector machines in digital mammograms. Int J Comput Assist Radiol Surg 4(1):11–25, 2009CrossRefPubMedGoogle Scholar
  18. 18.
    Tang J, Rangayyan RM, Xu J, Naqa IE, Yang Y: Computer-aided detection and diagnosis of breast cancer with mammography: Recent advances. IEEE Trans Inf Technol Biomed 13(2):236–251, 2009CrossRefPubMedGoogle Scholar
  19. 19.
    van Dijck JAAM, Verbeek ALM, Hendriks JHCL, Holland R: The current detectability of breast cancer in a mammographic screening program. Cancer 72(6):1933–1938, 1993CrossRefPubMedGoogle Scholar
  20. 20.
    Sameti M, Ward RK, Morgan-Parkes J, Palcic B: Image feature extraction in the last screening mammograms prior to detection of breast cancer. IEEE J Select Topics Signal Process 3(1):46–52, 2009CrossRefGoogle Scholar
  21. 21.
    Rangayyan RM, Banik S, Prajna S, Desautels JEL: Detection of architectural distortion in prior mammograms of interval-cancer cases. Proceedings of the 23rd International Congress and Exhibition: Computer Assisted Radiology and Surgery, Berlin, Germany, June 2009, pp S171–S173Google Scholar
  22. 22.
    Banik S, Rangayyan RM, Desautels JEL: Detection of architectural distortion in prior mammograms of interval-cancer cases with neural networks. Proceedings of the 31st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Minneapolis, MN, September 2009, pp 6667–6670Google Scholar
  23. 23.
    Ayres FJ, Rangayyan RM: Design and performance analysis of oriented feature detectors. J Electron Imaging 16(2):023007, 1–12, 2007Google Scholar
  24. 24.
    Ayres FJ, Rangayyan RM: Characterization of architectural distortion in mammograms. IEEE Eng Med Biol Mag 24(1):59–67, 2005CrossRefPubMedGoogle Scholar
  25. 25.
    Anguiano E, Pancorbo MA, Aguilar M: Fractal characterization by frequency analysis: I. Surfaces. J Microsc 172:223–232, 1993Google Scholar
  26. 26.
    Aguilar M, Anguiano E, Pancorbo MA: Fractal characterization by frequency analysis: II. A new method. J Microsc 172:233–238, 1993Google Scholar
  27. 27.
    Haralick RM: Statistical and structural approaches to texture. Proc IEEE 67:786–804, 1979CrossRefGoogle Scholar
  28. 28.
    Haralick RM, Shanmugam K, Dinstein I: Textural features for image classification. IEEE Trans Syst Man Cybern 3(6):610–622, 1973CrossRefGoogle Scholar
  29. 29.
    American College of Radiology (ACR): Illustrated Breast Imaging Reporting and Data System (BI-RADS), 4th edition. Reston: American College of Radiology, 2003Google Scholar
  30. 30.
    Yankaskas BC, Schell MJ, Bird RE, Desrochers DA: Reassessment of breast cancers missed during routine screening mammography: A community based study. Am J Roentgenol 177:535–541, 2001Google Scholar
  31. 31.
    Burrell H, Evans A, Wilson A, Pinder S: False-negative breast screening assessment: What lessons we can learn? Clin Radiol 56:385–388, 2001CrossRefPubMedGoogle Scholar
  32. 32.
    Burrell HC, Sibbering DM, Wilson ARM, Pinder SE, Evans AJ, Yeoman LJ, Elston CW, Ellis IO, Blamey RW, Robertson JFR: Screening interval breast cancers: Mammographic features and prognostic factors. Radiology 199(4):811–817, 1996PubMedGoogle Scholar
  33. 33.
    Rangayyan RM: Biomedical Image Analysis, Boca Raton: CRC, 2005Google Scholar
  34. 34.
    Mudigonda NR, Rangayyan RM: Texture flow-field analysis for the detection of architectural distortion in mammograms. In: Ramakrishnan AG Ed. Proceedings of Biovision, Bangalore, India, December 2001, pp 76–81Google Scholar
  35. 35.
    Ayres FJ, Rangayyan RM: Characterization of architectural distortion in mammograms. Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (CD-ROM), Cancún, Mexico, September 2003, pp 886–889Google Scholar
  36. 36.
    Suckling J, Parker J, Dance DR, Astley S, Hutt I, Boggis CRM, Ricketts I, Stamakis E, Cerneaz N, Kok S-L, Taylor P, Betal D, Savage J: The Mammographic Image Analysis Society digital mammogram database. In: Gale AG, Astley SM, Dance DD, Cairns AY Eds. Digital Mammography: Proceedings of the 2nd International Workshop on Digital Mammography. York: Elsevier, 1994, pp. 375–378Google Scholar
  37. 37.
    Matsubara T, Ichikawa T, Hara T, Fujita H, Kasai S, Endo T, Iwase T: Automated detection methods for architectural distortions around skinline and within mammary gland on mammograms. In: Lemke HU, Vannier MW, Inamura K, Farman AG, Doi K, Reiber JHC Eds. Proceedings of the 17th International Congress and Exhibition on Computer Assisted Radiology and Surgery (CARS2003). London: Elsevier, 2003, pp. 950–955Google Scholar
  38. 38.
    Ichikawa T, Matsubara T, Hara T, Fujita H, Endo T, Iwase T: Automated detection method for architectural distortion areas on mammograms based on morphological processing and surface analysis. In: Fitzpatrick JM, Sonka M Eds. Proceedings of SPIE Medical Imaging 2004: Image Processing. San Diego: SPIE, 2004, pp. 920–925Google Scholar
  39. 39.
    Hara T, Makita T, Matsubara T, Fujita H, Inenaga Y, Endo T, Iwase T: Automated detection method for architectural distortion with spiculation based on distribution assessment of mammary gland on mammogram. In: Astley SM, Brady M, Rose C, Zwiggelaar R Eds. Digital Mammography/IWDM, volume 4046 of Lecture Notes in Computer Science Manchester, UK, June 2006, pp 370–375,Google Scholar
  40. 40.
    Matsubara T, Hara T, Fujita H, Endo T, Iwase T: Automated detection method for mammographic spiculated architectural distortion based on surface analysis. Proceedings of the 22nd International Congress and Exhibition on Computer Assisted Radiology and Surgery (CARS2008), volume 3(1), Barcelona, Spain, June 2008, pp S176–S177Google Scholar
  41. 41.
    Guo Q, Shao J, Ruiz V: Investigation of support vector machine for the detection of architectural distortion in mammographic images. J Phys: Conf Ser 15:88–94, 2005CrossRefGoogle Scholar
  42. 42.
    Sampat MP, Bovik AC: Detection of spiculated lesions in mammograms. Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (CD-ROM), Cancún, Mexico, September 2003, pp 810–813Google Scholar
  43. 43.
    Sampat MP, Whitman GJ, Markey MK, Bovik AC: Evidence based detection of spiculated masses and architectural distortion. In: Fitzpatrick JM, Reinhardt JM Eds. Proceedings of SPIE Medical Imaging 2005: Image Processing, volume 5747, San Diego, CA, 2005, pp 26–37Google Scholar
  44. 44.
    Özekes S, Osman O, Çamurcu AY: Computerized detection of architectural distortions in digital mammograms. Proceedings of the 19th International Conference on Computer Assisted Radiology and Surgery (CARS 2005), volume 1281, Berlin, Germany, 2005, p 1396Google Scholar
  45. 45.
    Eltonsy N, Tourassi G, Elmaghraby A: Investigating performance of a morphology-based CAD scheme in detecting architectural distortion in screening mammograms. In: Lemke HU, Inamura K, Doi K, Vannier MW, Farman AG Eds. Proceedings of the 20th International Congress and Exhibition on Computer Assisted Radiology and Surgery (CARS 2006). Osaka: Springer, 2006, pp. 336–338Google Scholar
  46. 46.
    Nakayama R, Watanabe R, Kawamura T, Takada T, Yamamoto K, Takeda K: Computer-aided diagnosis scheme for detection of architectural distortion on mammograms using multiresolution analysis. Proceedings of the 21st International Congress and Exhibition on Computer Assisted Radiology and Surgery (CARS 2008), volume 3(1), Barcelona, Spain, June 2008, page S418–S419Google Scholar
  47. 47.
    Nemoto M, Honmura S, Shimizu A, Furukawa D, Kobatake H, Nawano S: A pilot study of architectural distortion detection in mammograms based on characteristics of line shadows. Int J Comput Assist Radiol Surg 4(1):27–36, 2009CrossRefPubMedGoogle Scholar
  48. 48.
    Burhenne LJW, Wood SA, D’Orsi CJ, Feig SA, Kopans DB, O’Shaughnessy KF, Sickles EA, Tabar L, Vyborny CJ, Castellino RA: Potential contribution of computer-aided detection to the sensitivity of screening mammography. Radiology 215(2):554–562, 2000Google Scholar
  49. 49.
    Evans WP, Burhenne LJW, Laurie L, O’Shaughnessy KF, Castellino RA: Invasive lobular carcinoma of the breast: Mammographic characteristics and computer-aided detection. Radiology 225(1):182–189, 2002CrossRefPubMedGoogle Scholar
  50. 50.
    Birdwell RL, Ikeda DM, O’Shaughnessy KF, Sickles EA: Mammographic characteristics of 115 missed cancers later detected with screening mammography and the potential utility of computer-aided detection. Radiology 219(1):192–202, 2001PubMedGoogle Scholar
  51. 51.
    Bird RE, Wallace TW, Yankaskas BC: Analysis of cancers missed at screening mammography. Radiology 184(3):613–617, 1992PubMedGoogle Scholar
  52. 52.
    Sameti M, Morgan-Parkes J, Ward RK, Palcic B: Classifying image features in the last screening mammograms prior to detection of a malignant mass. In: Karssemeijer N, Thijssen M, Hendriks J, van Erning L Eds. Proceedings of the 4th International Workshop on Digital Mammography. Nijmegen, The Netherlands, 1998 pp 127–134Google Scholar
  53. 53.
    Burnside ES, Sickles EA, Sohlich RE, Dee KE: Differential value of comparison with previous examinations in diagnostic versus screening mammography. Am J Roentgenol 179:1173–1177, 2002Google Scholar
  54. 54.
    Sumkin JH, Holbert BL, Herrmann JS, Hakim CA, Ganott MA, Poller WR, Shah R, Hardesty LA, Gur D: Optimal reference mammography: A comparison of mammograms obtained 1 and 2 years before the present examination. Am J Roentgenol 180:343–346, 2003Google Scholar
  55. 55.
    Varela C, Karssemeijer N, Hendriks JHCL, Holland R: Use of prior mammograms in the classification of benign and malignant masses. Eur J Radiol 56:248–255, 2005CrossRefPubMedGoogle Scholar
  56. 56.
    Zheng B, Good WF, Armfield DR, Cohen C, Hertzberg T, Sumkin JH, Gur D: Performance change of mammographic CAD schemes optimized with most-recent and prior image databases. Acad Radiol 10:283–288, 2003CrossRefPubMedGoogle Scholar
  57. 57.
    Petrick N, Chan HP, Sahiner B, Helvie MA, Paquerault S: Evaluation of an automated computer-aided diagnosis system for the detection of masses on prior mammograms. Proceedings of SPIE Volume 3979, Medical Imaging 2000: Image Processing, 2000, pp 967–973Google Scholar
  58. 58.
    Garvican L, Field S: A pilot evaluation of the R2 Image Checker System and users’ response in the detection of interval breast cancers on previous screening films. Clin Radiol 56:833–837, 2001CrossRefPubMedGoogle Scholar
  59. 59.
    Ikeda DM, Birdwell RL, O’Shaughnessy KF, Sickles EA, Brenner RJ: Computer-aided detection output on 172 subtle findings on normal mammograms previously obtained in women with breast cancer detected at follow-up screening mammography. Radiology 230:811–819, 2004CrossRefPubMedGoogle Scholar
  60. 60.
    Ciatto S, Del Turco MR, Burke P, Visioli C, Paci E, Zappa M: Comparison of standard and double reading and computer-aided detection (CAD) of interval cancers at prior negative screening mammograms: Blind review. Br J Cancer 89:1645–1649, 2003CrossRefPubMedGoogle Scholar
  61. 61.
    Moberg K, Bjurstam N, Wilczek B, Rostgård L, Egge E, Muren C: Computer assisted detection of interval breast cancers. Eur J Radiol 39:104–110, 2001CrossRefPubMedGoogle Scholar
  62. 62.
    Majid AS, de Paredes ES, Doherty RD, Sharma NR, Salvador X: Missed breast carcinoma: Pitfalls and pearls. RadioGraphics 23:881–895, 2003CrossRefPubMedGoogle Scholar
  63. 63.
    Otsu N: A threshold selection method from gray-level histograms. IEEE Trans Syst Man Cybern 9(1):62–66, 1979CrossRefGoogle Scholar
  64. 64.
    Gonzalez RC, Woods RE: Digital Image Processing, 2nd edition. Upper Saddle River: Prentice-Hall, 2002Google Scholar
  65. 65.
    Gabor D: Theory of communication. J Inst Electr Eng 93:429–457, 1946Google Scholar
  66. 66.
    Ferrari RJ, Rangayyan RM, Desautels JEL, Frère AF: Analysis of asymmetry in mammograms via directional filtering with Gabor wavelets. IEEE Trans Med Imag 20(9):953–964, 2001CrossRefGoogle Scholar
  67. 67.
    Manjunath BS, Ma WY: Texture features for browsing and retrieval of image data. IEEE Trans Pattern Anal Mach Intell 18(8):837–842, 1996CrossRefGoogle Scholar
  68. 68.
    Rao AR: A Taxonomy for Texture Description and Identification, New York: Springer, 1990Google Scholar
  69. 69.
    Rao AR, Jain RC: Computerized flow field analysis: Oriented texture fields. IEEE Trans Pattern Anal Mach Intell 14(7):693–709, 1992CrossRefGoogle Scholar
  70. 70.
    Peitgen H-O, Jürgens H, Saupe D: Chaos and Fractals: New Frontiers of Science, 2nd edition. New York: Springer, 2004Google Scholar
  71. 71.
    Rangayyan RM, Nguyen TM: Fractal analysis of contours of breast masses in mammograms. J Digit Imaging 20(3):223–237, 2007CrossRefPubMedGoogle Scholar
  72. 72.
    Bak P, Tang C, Wiesenfeld K: Self-organized criticality: An explanation of 1/f noise. Am Phys Soc 59:381–384, 1987Google Scholar
  73. 73.
    Lowen SB, Teich MC: Fractal renewal processes generate 1/f noise. Am Phys Soc 47:992–1001, 1993Google Scholar
  74. 74.
    Billock VA, De Guzman GC, Kelso JAS: Fractal time and 1/f spectra in dynamic images and human vision. Physica D: Nonlinear Phenomena 148:136–146, 2001CrossRefGoogle Scholar
  75. 75.
    Fortin C, Kumaresan R, Ohley W: Fractal dimension in the analysis of medical images. IEEE Eng Med Biol Mag 11:65–71, June 1992CrossRefGoogle Scholar
  76. 76.
    Duda RO, Hart PE, Stork DG: Pattern Classification, 2nd edition. New York: Wiley-Interscience, 2001Google Scholar
  77. 77.
    Sahiner B, Chan H-P, Petrick N, Wagner RF, Hadjiiski L: Feature selection and classifier performance in computer-aided diagnosis: The effect of finite sample size. Med Phys 27(7):1509–1522, 2000CrossRefPubMedGoogle Scholar
  78. 78.
    Nandi RJ, Nandi AK, Rangayyan RM, Scutt D: Classification of breast masses in mammograms using genetic programming and feature selection. Med Biol Eng Comput 44:683–694, 2006CrossRefPubMedGoogle Scholar
  79. 79.
    Ware JH, Mosteller F, Delgado F, Donnelly C, Ingelfinger JA: P values. In: Bailar III, JC, Mosteller F Eds. Medical Uses of Statistics. 2nd edition. Boston: NEJM Books, 1992, pp. 181–200Google Scholar
  80. 80.
    Metz CE: ROC methodology in radiologic imaging. Invest Radiol 21:720–733, 1986CrossRefPubMedGoogle Scholar
  81. 81.
    Metz CE: Basic principles of ROC analysis. Semin Nucl Med VIII(4):283–298, 1978CrossRefGoogle Scholar
  82. 82.
    Mu T, Nandi AK, Rangayyan RM: Classification of breast masses using selected shape, edge-sharpness, and texture features with linear and kernel-based classifiers. J Digit Imaging 21(2):153–169, 2008CrossRefPubMedGoogle Scholar
  83. 83.
    Press WH, Teukolsky SA, Vetterling WT, Flannery BP: Numerical Recipes in C, 2nd edition. New Delhi: Cambridge University Press, 1988Google Scholar
  84. 84.
    Bornefalk H, Hermansson AB: On the comparison of FROC curves in mammography CAD systems. Med Phys 32(2):412–417, 2005CrossRefPubMedGoogle Scholar
  85. 85.
    Miller H: The FROC curve: A representation of the observer’s performance for the method of free response. J Acoust Soc Am 46(6B):1473–1476, 1969CrossRefPubMedGoogle Scholar
  86. 86.
    Ramsey FL, Schafer DW: The Statistical Sleuth: A Course in Methods of Data Analysis, Belmont: Duxbury Press, 1997Google Scholar
  87. 87.
    Schalkoff R: Pattern Recognition: Statistical, Structural and Neural Approaches, New York: Wiley, 1992Google Scholar
  88. 88.
    Haykin S: Neural Networks: A Comprehensive Foundation, 2nd edition. Upper Saddle River: Prentice Hall, 1999Google Scholar
  89. 89.
    Wasserman PD: Advanced Methods in Neural Computing, New York: Van Nostrand Reinhold, 1993Google Scholar
  90. 90.
    Vapnik V: Statistical Learning Theory, New York: Wiley, 1998Google Scholar
  91. 91.
    Schölkopf B, Smola AJ: Learning with Kernels—Support Vector Machines, Regularization, Optimization, and Beyond, Cambridge: MIT Press, 2002Google Scholar
  92. 92.
    Fransens R, Prins JD, Gool LV: SVM-based nonparametric discriminant analysis, an application to face detection. Proceedings of the Ninth IEEE International Conference on Computer Vision (ICCV 2003), volume 2. IEEE Computer Society, October 2003, pages 1289–1296Google Scholar
  93. 93.
    Burges CJC: A tutorial on support vector machines for pattern recognition. Data Min Knowl Discov 2(2):121–167, 1998CrossRefGoogle Scholar
  94. 94.
    Alto H, Rangayyan RM, Paranjape RB, Desautels JEL, Bryant H: An indexed atlas of digital mammograms for computer-aided diagnosis of breast cancer. Ann Télécommun 58(5–6):820–835, 2003Google Scholar
  95. 95.
    Alberta Cancer Board, Alberta, Canada. Screen Test: Alberta Program for the Early Detection of Breast Cancer—2001/03 Biennial Report, 2004.
  96. 96.
    ROCKIT: Kurt Rossmann Laboratories for Radiologic Image Research. ROC Software. Last accessed on July 20, 2009
  97. 97.
    Kundel HL, Berbaum K, Dorfman D, Gur D, Metz CE, Swensson RG Eds. Journal of the ICRU. ICRU Report 79: Receiver Operating Characteristic Analysis in Medical Imaging, volume 8(1), Chapter 5. Extensions to Conventional ROC Methodology: LROC, FROC, and AFROC. Oxford: Oxford University Press, April 2008, pp 31–35Google Scholar
  98. 98.
    Chakraborty DP: Statistical power in observer-performance studies: Comparison of the receiver operating characteristic and free-response methods in tasks involving localization. Acad Radiol 9(2):147–156, 2002CrossRefPubMedGoogle Scholar
  99. 99.
    Ferrari RJ, Rangayyan RM, Desautels JEL, Borges RA, Frère AF: Automatic identification of the pectoral muscle in mammograms. IEEE Trans Med Imag 23:232–245, 2004CrossRefGoogle Scholar
  100. 100.
    Ferrari RJ, Rangayyan RM, Borges RA, Frère AF: Segmentation of the fibroglandular disc in mammograms using Gaussian mixture modeling. Med Biol Eng Comput 42:378–387, 2004CrossRefPubMedGoogle Scholar
  101. 101.
    Li J, Yau WY, Wang H: Constrained nonlinear models of fingerprint orientations with prediction. Pattern Recogn 39(1):102–114, 2006CrossRefGoogle Scholar
  102. 102.
    Kinsner W: A unified approach to fractal dimensions. Proceedings of the Fourth IEEE International Conference on Cognitive Informatics (ICCI), Irvine, CA, August 2005. IEEE Computer Society, pp 58–72Google Scholar

Copyright information

© Society for Imaging Informatics in Medicine 2009

Authors and Affiliations

  • Rangaraj M. Rangayyan
    • 1
    • 2
    Email author
  • Shantanu Banik
    • 1
  • J. E. Leo Desautels
    • 1
  1. 1.Department of Electrical and Computer EngineeringSchulich School of EngineeringCalgaryCanada
  2. 2.Department of RadiologyUniversity of CalgaryCalgaryCanada

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