JoVE Logo
Faculty Resource Center

Sign In

Summary

Abstract

Introduction

Protocol

Representative Results

Discussion

Acknowledgements

References

Medicine

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Published: August 30th, 2013

DOI:

10.3791/50341

1Department of Electrical and Computer Engineering, Schulich School of Engineering, University of Calgary , 2Department of Radiology, University of Calgary

We demonstrate methods for the detection of architectural distortion in prior mammograms. Oriented structures are analyzed using Gabor filters and phase portraits to detect sites of radiating tissue patterns. Each site is characterized and classified using measures to represent spiculating patterns. The methods should assist in the detection of breast cancer.

We demonstrate methods for the detection of architectural distortion in prior mammograms of interval-cancer cases based on analysis of the orientation of breast tissue patterns in mammograms. We hypothesize that architectural distortion modifies the normal orientation of breast tissue patterns in mammographic images before the formation of masses or tumors. In the initial steps of our methods, the oriented structures in a given mammogram are analyzed using Gabor filters and phase portraits to detect node-like sites of radiating or intersecting tissue patterns. Each detected site is then characterized using the node value, fractal dimension, and a measure of angular dispersion specifically designed to represent spiculating patterns associated with architectural distortion.

Our methods were tested with a database of 106 prior mammograms of 56 interval-cancer cases and 52 mammograms of 13 normal cases using the features developed for the characterization of architectural distortion, pattern classification via quadratic discriminant analysis, and validation with the leave-one-patient out procedure. According to the results of free-response receiver operating characteristic analysis, our methods have demonstrated the capability to detect architectural distortion in prior mammograms, taken 15 months (on the average) before clinical diagnosis of breast cancer, with a sensitivity of 80% at about five false positives per patient.

Breast cancer is a major disease affecting women and is the second leading cause of cancer related death among women 1,2. In order to improve the chance of survival and the prognosis of the affected patients through effective treatment at early stages of breast cancer, the disease needs to be detected as early as possible. In retrospective analysis of cases of breast cancer, subtle signs of abnormalities have been observed on previously acquired screening mammograms 3,4. Architectural distortion is one such localized mammographic sign of possibly early stages of breast cancer that is difficult to detect 5,6. The associated patterns are....

Log in or to access full content. Learn more about your institution’s access to JoVE content here

1. Overview of Methodology

In our procedure, potential sites of architectural distortion in mammograms are automatically detected via analysis of oriented textural patterns with the application of a bank of Gabor filters 26 and modeling of phase portraits 11,27. The detected sites are then processed through the steps of extraction of features or measures to characterize architectural distortion, development of a trained classifier, and application of an algorithm f.......

Log in or to access full content. Learn more about your institution’s access to JoVE content here

The three features, namely, node value, FD, and HF , provided AUC values of 0.61, 0.59, and 0.64, respectively, when each feature was used on its own. Combined use of the three features provided improved performance with AUC = 0.70. The FROC curve obtained with the combination of the three features is shown in Figure 11, which indicates a sensitivity of 80% at 5.6 FPs/patient and 89% at 7.5 FPs/patient. Use of only the node value provided a sensitivity of 80% at 8.1 FPs/patient and 89.......

Log in or to access full content. Learn more about your institution’s access to JoVE content here

We have presented a series of sophisticated techniques of digital image processing and pattern recognition, also known as machine learning and CAD, for the detection of architectural distortion in prior mammograms of interval-cancer cases. The methods are based on analysis of the oriented textural patterns present in mammographic images. Our methods, including several more features proposed in our related works, are capable of detecting early signs of breast cancer 15 months ahead of the time of clinical diagnosis, on th.......

Log in or to access full content. Learn more about your institution’s access to JoVE content here

This work was supported by grants from the Collaborative Research and Training Experience Programme (CREATE) and a Discovery Grant from the Natural Sciences and Engineering Research Council (NSERC) of Canada.

....

Log in or to access full content. Learn more about your institution’s access to JoVE content here

  1. Tang, J., Rangayyan, R. M., Xu, J., El-Naqa, I., Yang, Y. Computer-aided detection and diagnosis of breast cancer with mammography: Recent advances. IEEE Transactions on Information Technology in Biomedicine. 13 (2), 236-251 (2009).
  2. van Dijck, J. A. A. M., Verbeek, A. L. M., Hendriks, J. H. C. L., Holland, R. The current detectability of breast cancer in a mammographic screening program. Cancer. 72 (6), 1933-1938 (1993).
  3. Rangayyan, R. M., Prajna, S., Ayres, F. J., Desautels, J. E. L. Detection of architectural distortion in mammograms acquired prior to the detection of breast cancer using Gabor filters, phase portraits, fractal dimension, and texture analysis. International Journal of Computer Assisted Radiology and Surgery. 2 (6), 347-361 (2008).
  4. Homer, M. J. . Mammographic Interpretation: A Practical Approach. , (1997).
  5. Knutzen, A. M., Gisvold, J. J. Likelihood of malignant disease for various categories of mammographically detected, nonpalpable breast lesions. Mayo Clinic Proceedings. 68, 454-460 (1993).
  6. Rangayyan, R. M., Ayres, F. J., Desautels, J. E. L. A review of computer-aided diagnosis of breast cancer: Toward the detection of subtle signs. Journal of the Franklin Institute. 344, 312-348 (2007).
  7. Doi, K. Diagnostic imaging over the last 50 years: research and development in medical imaging science and technology. Physics in Medicine and Biology. 51, R5-R27 (2006).
  8. Rangayyan, R. M. . Biomedical Image Analysis. , (2005).
  9. Rangayyan, R. M., Ayres, F. J. Gabor filters and phase portraits for the detection of architectural distortion in mammograms. Medical and Biological Engineering and Computing. 44, 883-894 (2006).
  10. Ayres, F. J., Rangayyan, R. M. Reduction of false positives in the detection of architectural distortion in mammograms by using a geometrically constrained phase portrait model. International Journal of Computer Assisted Radiology and Surgery. 1, 361-369 (2007).
  11. Karssemeijer, N., te Brake, G. M. Detection of stellate distortions in mammograms. IEEE Transactions on Medical Imaging. 15 (5), 611-619 (1996).
  12. Guo, Q., Shao, J., Ruiz, V. F. Characterization and classification of tumor lesions using computerized fractal-based texture analysis and support vector machines in digital mammograms. International Journal of Computer Assisted Radiology and Surgery. 4 (1), 11-25 (2009).
  13. Sampat, M. P., Whitman, G. J., Markey, M. K., Bovik, A. C., Fitzpatrick, J. M., Reinhardt, J. M. Evidence based detection of spiculated masses and architectural distortion. Proceedings of SPIE Medical Imaging 2005: Image Processing. 5747, 26-37 (2005).
  14. Tourassi, G. D., Delong, D. M., Floyd Jr, ., E, C. A study on the computerized fractal analysis of architectural distortion in screening mammograms. Physics in Medicine and Biology. 51 (5), 1299-1312 (2006).
  15. 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. International Journal of Computer Assisted Radiology and Surgery. 4 (1), 27-36 (2009).
  16. Matsubara, T., Hara, T., Fujita, H., Endo, T., Iwase, T. Automated detection method for mammographic spiculated architectural distortion based on surface analysis. 3 (1), 176-177 (2008).
  17. Baker, J. A., Rosen, E. L., Lo, J. Y., Gimenez, E. I., Walsh, R., Soo, M. S. Computer-aided detection (CAD) in screening mammography: Sensitivity of commercial CAD systems for detecting architectural distortion. American Journal of Roentgenology. 181, 1083-1088 (2003).
  18. Sameti, M., Ward, R. K., Morgan-Parkes, J., Palcic, B. Image feature extraction in the last screening mammograms prior to detection of breast cancer. IEEE Journal of Selected Topics in Signal Processing. 3 (1), 46-52 (2009).
  19. Rangayyan, R. M., Banik, S., Desautels, J. E. L. Computer-aided detection of architectural distortion in prior mammograms of interval cancer. Journal of Digital Imaging. 23 (5), 611-631 (2010).
  20. Banik, S., Rangayyan, R. M., Desautels, J. E. L. Detection of architectural distortion in prior mammograms. IEEE Transactions on Medical Imaging. 30 (2), 279-294 (2011).
  21. Banik, S., Rangayyan, R. M., Desautels, J. E. L. Measures of angular spread and entropy for the detection of architectural distortion in prior mammograms. International Journal of Computer Assisted Radiology and Surgery. 8, 121-134 (2013).
  22. Broeders, M. J. M., Onland-Moret, N. C., Rijken, H. J. T. M., Hendriks, J. H. C. L., Verbeek, A. L. M., Holland, R. Use of previous screening mammograms to identify features indicating cases that would have a possible gain in prognosis following earlier detection. European Journal of Cancer. 39, 1770-1775 (2003).
  23. Alto, H., Rangayyan, R. M., Paranjape, R. B., Desautels, J. E. L., Bryant, H. An indexed atlas of digital mammograms for computer-aided diagnosis of breast cancer. Annales des Télécommunications. (5-6), 820-835 (2003).
  24. Gabor, D. Theory of communication. Journal of the Institute of Electrical Engineers. 93, 429-457 (1946).
  25. Rao, A. R. . A Taxonomy for Texture Description and Identification. , (1990).
  26. Otsu, N. A threshold selection method from gray-level histograms. IEEE Transactions on Systems, Man, and Cybernetics. 9 (1), 62-66 (1979).
  27. Gonzalez, R. C., Woods, R. E. . Digital Image Processing. , (2002).
  28. Ayres, F. J., Rangayyan, R. M. Design and performance analysis of oriented feature detectors. Journal of Electronic Imaging. 16 (2), (2007).
  29. Samulski, M., Karssemeijer, N. Optimizing case-based detection performance in a multiview CAD system for mammography. IEEE Transactions on Medical Imaging. 30 (4), 1001-1009 (2011).
  30. Muralidhar, G. S., Bovik, A. C., Giese, J. D., Sampat, M. P., Whitman, G. J., Haygood, T. M., Stephens, T. W., Markey, M. K. Snakules: a model-based active contour algorithm for the annotation of spicules on mammography. IEEE Transactions of Medical Imaging. 29 (10), 1768-1780 (2010).
  31. Ayres, F. J., Rangayyan, R. M., Hozman, J., Kneppo, P. Detection of architectural distortion in mammograms via analysis of phase portraits and curvilinear structures. Proceedings of EMBEC'05: 3rd European Medical & Biological Engineering Conference. 11, 1768-1773 (2005).
  32. Ferrari, R. J., Rangayyan, R. M., Desautels, J. E. L., Frère, A. F. Analysis of asymmetry in mammograms via directional filtering with Gabor wavelets. IEEE Transactions on Medical Imaging. 20 (9), 953-964 (2001).
  33. Zwiggelaar, R., Astley, S. M., Boggis, C. R. M., Taylor, C. J. Linear structures in mammographic images: Detection and classification. IEEE Transactions on Medical Imaging. 23 (9), 1077-1086 (2004).
  34. Ferrari, R. J., Rangayyan, R. M., Borges, R. A., Frère, A. F. Segmentation of the fibro-glandular disc in mammograms using Gaussian mixture modeling. Medical and Biological Engineering and Computing. 42, 378-387 (2004).
  35. Ichikawa, T., Matsubara, T., Hara, T., Fujita, H., Endo, T., Iwase, T., Fitzpatrick, J. M., Sonka, M. . Automated detection method for architectural distortion areas on mammograms based on morphological processing and surface analysis. , 920-923 (2004).
  36. Sonka, M., Hlavac, V., Boyle, R. . Image Processing, Analysis and Machine Vision. , (1993).
  37. Canny, J. A computational approach to edge detection. IEEE Transactions on Pattern Analysis and Machine Intelligence. 8 (6), 679-698 (1986).
  38. Rao, A. R., Jain, R. C. Computerized flow field analysis: Oriented texture fields. IEEE Transactions on Pattern Analysis and Machine Intelligence. 14 (7), 693-709 (1992).
  39. Kirkpatrick, S., Gelatt, C. D., Vecchi, M. P. Optimization by simulated annealing. Science. 220 (4598), 671-680 (1983).
  40. Gershenfeld, N. . The Nature of Mathematical Modeling. , (1999).
  41. Mandelbrot, B. B. The Fractal Geometry of Nature. , (1983).
  42. Peitgen, H. -. O., Jürgens, H., Saupe, D. . Chaos and Fractals: New Frontiers of Science. , (2004).
  43. Fortin, C., Kumaresan, R., Ohley, W. Fractal dimension in the analysis of medical images. IEEE Engineering in Medicine and Biology Magazine. 11, 65-71 (1992).
  44. Schepers, H. E., van Beek, J. H. G. M., Bassingthwaighte, J. B. Four methods to estimate the fractal dimension from self-affine signals. IEEE Engineering in Medicine and Biology Magazine. 11, 57-64 (1992).
  45. Bak, P., Tang, C., Wiesenfeld, K. Self-organized criticality: An explanation of 1/f noise. The American Physical Society. 59, 381-384 (1987).
  46. Billock, V. A., De Guzman, G. C., Kelso, J. A. S. Fractal time and 1/f spectra in dynamic images and human vision. Physica D: Nonlinear Phenomena. 148, 136-146 (2001).
  47. Anguiano, E., Pancorbo, M. A., Aguilar, M. Fractal characterization by frequency analysis: I. Surfaces. Journal of Microscopy. 172, 223-232 (1993).
  48. Aguilar, M., Anguiano, E., Pancorbo, M. A. Fractal characterization by frequency analysis: II. A new method. Journal of Microscopy. 172, 233-238 (1993).
  49. Metz, C. E. ROC methodology in radiologic imaging. Investigative Radiology. 21, 720-733 (1986).
  50. Bornefalk, H., Hermansson, A. B. On the comparison of FROC curves in mammography CAD systems. Medical Physics. 32 (2), 412-417 (2005).
  51. Miller, H. The FROC curve: A representation of the observer's performance for the method of free response. Journal of the Acoustical Society of America. 46, 1473-1476 (1969).
  52. Chakraborty, D. P. Statistical power in observer-performance studies: Comparison of the receiver operating characteristic and free-response methods in tasks involving localization. Academic Radiology. 9 (2), 147-156 (2002).
  53. Ramsey, F. L., Schafer, D. W. . The Statistical Sleuth: A Course in Methods of Data Analysis. , (1997).
  54. Wiley-Interscience, . , (2001).
  55. Rangayyan, R. M., Banik, S., Chakraborty, J., Mukhopadhyay, S., Desautels, J. E. L. Measures of divergence of oriented patterns for the detection of architectural distortion in prior mammograms. International Journal of Computer Assisted Radiology and Surgery. , (2013).
  56. Burhenne, L. J. W., Wood, S. A., D'Orsi, C. J., Feig, S. A., Kopans, D. B., O'Shaughnessy, K. F., Sickles, E. A., Tabar, L., Vyborny, C. J., Castellino, R. A. Potential contribution of computer-aided detection to the sensitivity of screening mammography. Radiology. 215 (2), 554-562 (2000).
  57. Birdwell, R. L., Ikeda, D. M., O'Shaughnessy, K. F., Sickles, E. A. Mammographic characteristics of 115 missed cancers later detected with screening mammography and the potential utility of computeraided detection. Radiology. 219 (1), 192-202 (2001).

This article has been published

Video Coming Soon

JoVE Logo

Privacy

Terms of Use

Policies

Research

Education

ABOUT JoVE

Copyright © 2024 MyJoVE Corporation. All rights reserved