Frame Texture Classification Method (FTCM) Applied on Mammograms for Detection of Abnormalities

Texture classification is an important image processing task with a broad application range. Many different techniques for texture classification have been explored. Using sparse approximation as a feature extraction method for texture classification is a relatively new approach, and Skretting et al. recently presented the Frame Texture Classification Method (FTCM), showing very good results on classical texture images. As an extension of that work the FTCM is here tested on a real world application as detection of abnormalities in mammograms. Some extensions to the original FTCM that are useful in some applications are implemented; two different smoothing techniques and a vector augmentation technique. Both detection of microcalcifications (as a primary detection technique and as a last stage of a detection scheme), and soft tissue lesions in mammograms are explored. All the results are interesting, and especially the results using FTCM on regions of interest as the last stage in a detection scheme for microcalcifications are promising.

Authors:

• Kjersti Engan
• Karl Skretting
• Jostein Herredsvela
• Thor Ole Gulsrud

Keywords:

• detection
• mammogram
• texture classification
• dictionary learning
• FTCM

References:

[1] M. Tuceryan and A. K. Jain, "Texture analysis," in Handbook of Pattern
Recognition and Computer Vision, C. H. Chen, L. F. Pau, and P. S. P.
Wang, Eds. Singapore: World Scientific Publishing Co, 1998, ch. 2.1,
pp. 207-248.
[2] K. Skretting and J. H. Hus├©y, "Texture classification using sparse
frame-based representations," EURASIP Journal on Applied Signal
Processing, vol. 2006, pp. Article ID 52 561, 11 pages, 2006,
doi:10.1155/ASP/2006/52561.
[3] www.kreftregisteret.no.
[4] H. P. Chan, K. Doi, C. Vyborny, R. Scmidt, C. Metz, K. L. Lam,
T. Ogura, Y. Wu, and H. Macmahon, "Improvement in radiologists-
detection of clustered microcalcifications on mammograms. the potential
of computer-aided diagnosis," Investigative Radiology, vol. 25, no. 10,
pp. 1102-1110, 1990.
[5] H. P. Chany, B. Sahiner, and N. P. et al., "Computerized classification
of malignant and benign microcalcifications on mammograms: texture
analysis using an artificial neural network," Phys. Med. Biol., vol. 42,
no. 3, pp. 549-567, Jan. 1997.
[6] N. R. Mudigonda, R. M. Rangayyan, and J. E. L. Desautels, "Gradient
and texture analysis for the classification of mammographic masses,"
IEEE Trans. Medical Imaging, vol. 19, no. 10, pp. 1032-1043, Oct.
2000.
[7] A. Gersho and R. M. Gray, Vector Quantization and Signal Compression.
Boston, USA: Kluwer Academic Publishers, 1992.
[8] B. K. Natarajan, "Sparse approximate solutions to linear systems," SIAM
journal on computing, vol. 24, pp. 227-234, Apr. 1995.
[9] S. G. Mallat and Z. Zhang, "Matching pursuits with time-frequency
dictionaries," IEEE Trans. Signal Processing, vol. 41, pp. 3397-3415,
Dec. 1993.
[10] M. Gharavi-Alkhansari and T. S. Huang, "A fast orthogonal matching
pursuit algorithm," in Int. Conf. on Acoust. Speech and Signal Proc.,
Seattle, U.S.A, May 1998, pp. 1389-1392.
[11] K. Skretting and J. H. Hus├©y, "Partial search vector selection for sparse
signal representation," in NORSIG-03, Bergen, Norway, Oct. 2003,
available at http://www.ux.his.no/˜karlsk/.
[12] S. S. Chen, "Basis pursuit," Ph.D. dissertation, Stanford University, Nov.
1995.
[13] B. D. Rao and K. Kreutz-Delgado, "An affine scaling methodology for
best basis selection," IEEE Trans. Signal Processing, vol. 47, pp. 187-
200, Jan. 1999.
[14] B. D. Rao, K. Engan, S. F. Cotter, J. Palmer, and K. Kreutz-Delgado,
"Subset selection in noise based on diversity measure minimization,"
IEEE Trans. Signal Processing, vol. 51, no.3, pp. 760-770, Mar. 2003.
[15] K. Engan, S. O. Aase, and J. H. Hus├©y, "Method of optimal directions
for frame design," in Proc. ICASSP -99, Phoenix, USA, Mar. 1999, pp.
2443-2446.
[16] ÔÇöÔÇö, "Multi-frame compression: Theory and design," Signal Processing,
vol. 80, pp. 2121-2140, Oct. 2000.
[17] K. Engan, K. Skretting, and J. Hus├©y, "A family of iterative LS-based
dictionary learning algorithms, ILS-DLA, for sparse signal representation,"
Digital Signal Processing, Elsevier, vol. 17, no. 1, pp. 32-49,
2007, doi:10.1016/j.dsp.2006.02.002.
[18] K. Skretting, "Sparse signal representation using overlapping frames,"
Ph.D. dissertation, Stavanger University College/NTNU, Stavanger, Norway,
2002, available at http://www.ux.his.no/˜karlsk/.
[19] K. Skretting and J. Hus├©y, "Frame based texture classification by considering
various spatial neighborhoods," in Proc. NORSIG -05, Stavanger,
Norway, Sept. 2005.
[20] E. R. Dougherty and R. A. Lotufo, Hands-on Morphological Image
Processing. Washington, USA: SPIE PRESS, 2003.
[21] T. O. Gulsrud and J. H. Hus├©y, "Optimal filter based detection of
microcalcifications," IEEE Transactions on Biomedical Engineering,
vol. 48, no. 11, pp. 1272-1280, 2001.
[22] K. Engan, T. O. Gulsrud, K. F. Fretheim, B. Iversen, and L. Eriksen,
"A complete computer aided detection (CAD) system for microcalcifications
in mammograms - MammoScan ╬╝CaD," Submitted for journal
publication.
[23] K. Engan and T. O. Gulsrud, "VarMet - a method for detection of image
singularities with application to mammography," WSEAS Transactions
on Signal Processing, vol. 2, no. 9, pp. 1222-1229, Sept. 2006.
[24] G. Horvath, J. Valyon, G. Strausz, M. Pataki, L. Sragner, L. Lasztovicza,
and N. Szekely, "Intelligent advisory system for screening mammography,"
in Proceedings of IEEE Instrumentation and Measurement
Technology Conference, IMTC 04, vol. 3, May 2004, pp. 2071-2076.
[25] I. El-Naqa, Y. Yang, M. W. N.P., Galatsanos, and R. Nishikawa, "A
support vector machine approach for detection of microcalcification,"
IEEE Trans. Medical Imaging, vol. 21, no. 12, pp. 1552-1563, 2002.
[26] W. J. H. Veldkamp and N. Karssemeijer, "An improved method for detection
of microcalcification clusters in digital mammograms," in Proc.
of SPIE International Symposium Medical Imaging, Image Processing
1999, vol. 3661, San Diego, Ca, USA, May 1999, pp. 512-522.
[27] www.r2tech.com.
[28] www.wiau.man.ac.uk/services/MIAS/MIAScom.html.
[29] J. Herredsvela, K. Engan, T. O. Gulsrud, and K. Skretting, "Detection
of lesions in mammograms using learned dictionaries and sparse representations,"
in Proc. of EMBEC05, Prague, Czech Republic, Nov. 2005.
[30] ÔÇöÔÇö, "Detection of masses in mammograms by watershed segmentation
and sparse representasions using learned dictionaries," in Proc. of
NORSIG 2005, Stavanger, Norway, Sept. 2005.
[31] G. M. te Brake and N. Karssemeijer, "Single and multiscale detection of
masses in digital mammograms," IEEE Trans. Medical Imaging, vol. 18,
no. 7, pp. 628-639, July 1999.
[32] H. Kobatake, M. Murakami, H. Takeo, and S. Nawano, "Computerized
detection of malignant tumors in digital mammograms," IEEE Trans.
Medical Imaging, vol. 18, no. 5, pp. 369-378, May 1999.