Detection of Breast Cancer in the JPEG2000 Domain

Authors: Fayez M. Idris, Nehal I. AlZubaidi

Abstract:

Breast cancer detection techniques have been reported to aid radiologists in analyzing mammograms. We note that most techniques are performed on uncompressed digital mammograms. Mammogram images are huge in size necessitating the use of compression to reduce storage/transmission requirements. In this paper, we present an algorithm for the detection of microcalcifications in the JPEG2000 domain. The algorithm is based on the statistical properties of the wavelet transform that the JPEG2000 coder employs. Simulation results were carried out at different compression ratios. The sensitivity of this algorithm ranges from 92% with a false positive rate of 4.7 down to 66% with a false positive rate of 2.1 using lossless compression and lossy compression at a compression ratio of 100:1, respectively.

Keywords: Breast cancer, JPEG2000, mammography, microcalcifications.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1332310

References:

[1] M. Nimry, Health experts plan major study on breast cancer patterns in jordan, Jordan Times (1998).
[2] S. J. Nass, I. C. Henderson and J. C. Lashof, "Mammography and beyond: Developing technologies for the early detection of breast cancer," INSTITUTE OF MEDICINE and Division of Earth and Life Studies NATIONAL RESEARCH COUNCIL, United States of America, Washington, DC, 2003, pp. 1-267.
[3] C. Fabregas, O. Egger, F. Moscheni and V. Vaerman, "Compression of medical images," Swiss Federal Institute of Technology EPFL, Switzerland, 1997, pp. 1-13.
[4] D. A. Clunie, Lossless compression of grayscale medical images - effectiveness of traditional and state of the art approaches, Quintiles Intelligent Imaging.
[5] W. M. Diyana, J. Larcher and R. Besar, A comparison of clustered microcalcifications automated detection methods in digital mammogram, IEEE International Conference on Acoustics, Speech, and Signal Processing Proceedings, 2003, pp. 385-388.
[6] J. M. Mossi and A. Albiol, Improving detection of clustered microcalcifications using morphological connected operators, IEEE Seventh International Conference on Image Processing and Its Applications, 1999.
[7] T. Netsch and H. Peitgen, Scale-space signatures for the detection of clustered microcalcifications in digital mammograms, IEEE Transactions on Medical Imaging 18 (1999), no. 9.
[8] H. Cheng, Y. M. Lui and R. I. Freimanis, A new approach to microcalcification detection in digital mammography, IEEE Transactions on Medical Imaging (1997).
[9] M. Gurcan, Y. Yardimci, A. Cetin and R. Ansari, Detection of microcalcifications in mammograms using higher order statistics, IEEE Signal Processing Letters 4 (1997), no. 8.
[10] M. Wilson, R. Hargrave, S. Mitra, Y. Shieh and G. H. Roberson, Automated detection of microcalcifications in mammograms through application of image pixel remapping and statistical filter.
[11] A. Wroblewska, P. Boninski, A. Przelaskowski and M. Kazubek, Segmentation and feature extraction for reliable classification of microcalcifications in digital mammograms, Opto-Electronics Review 11 (2003), no. 3, pp. 227-235.
[12] M. Melloul and L. Joskowicz, Segmentation of microcalcification in xray mammograms using entropy thresholding, Computer Assisted Radiology and Surgery CARS: Proceedings of the 16th International Congress and Exhibition 6 (2002), no. 671.
[13] M. J. Lado, P. G. Tahoces, A. J. Mendez, M. Souto and J. J. Vidal, Computer-assisted diagnosis: Application of wavelet transform techniques to the detection of clustered microcalcifications in digital mammograms, 10th Portuguese Conference on Pattern Recognition, Marzo, 1998, pp. 26-27.
[14] H. Yoshida, K. Doi, R. M. Nishikawa, K. Muto and M. Tsuda, "Application of wavelet transform to automated detection of clustered microcalcifications in digital mammograms," Tokyo Institute of Polytechnics 17, 1583 liyama, Atsugi, Kanagawa, Japan, 1994, pp. 24- 37.
[15] H. C. Choe and A. K. Chan, Microcalcification cluster detection in digitized mammograms using multiscale techniques, IEEE Southwest Symposium on Image Analysis and Interpretation, 1998, pp. 23-28.
[16] D. Nesbitt, F. Aghdasi, R. Ward and J. Morgan-Parkes, Detection of microcalcifications in digitized mammograms film images using wavelet enhancement and local adaptive false positive suppression, Proceedings. IEEE Pacific Rim Conference on Communications, Computers, and Signal Processing, 1995, pp. 594 - 597.
[17] T. C. Wang and N. B. Karayiannis, Detection of microcalcifications in digital mammograms using wavelets, IEEE Transactions on Medical Imaging 17 (1998), no. 4, pp. 498-509.
[18] A. Laine, M. Lewis and F. Taylor, A wavelet based mammographic system, IEEE International Conference on Acoustics, Speech, and Signal Processing, 1994,.
[19] T.J.Brown, An adaptive strategy for wavelet based image enhancement, Irish Machine Vision & Image Processing Conference IMVIP 2000 Programme, Image and Vision Systems Group, School of Computer Science, The Queen-s University of Belfast., 2000.
[20] L. Zhang and P. Bao, A wavelet-based edge detection method by scale multiplication, Proceedings of IEEE International Conference on Pattern Recognition, 2002, pp. 501-504.
[21] J. Z. Wang, Wavelets and imaging informatics: A review of the literature.
[22] "Jpeg2000 for medical applications link," Aware, Inc. the source for broadband intellectual property.
[23] "Jpeg-2000 home page.," URL http://www.jpeg.org/jpeg2000/index.html.
[24] M. Unser and T. Blu, Mathematical properties of the jpeg2000 wavelet filters, IEEE transactions on image processing 12 (2003), no. 9, pp. 1080-1090.
[25] W. Kou, Digital image compression algorithms and standards, Kluwer Academic Publishers, Boston/Dordrecht/London, 1995.
[26] R. C. Gonzalez and R. E. Woods, Digital image processing, Prentice Hall, New Jersey, 2002.
[27] A. Azimifar, P. Fieguth and E. Jernigan, Towards random field modeling of wavelet statistics, ICIP, IEEE, 2002.
[28] "Ddsm: Digital database for screening mammography.," University of South Florida Digital Mammography Home Page. Available from URL http://marathon.csee.usf.edu/Mammography/Database.html.