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.
[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.
[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.
@article{"International Journal of Medical, Medicine and Health Sciences:53572", author = "Fayez M. Idris and Nehal I. AlZubaidi", title = "Detection of Breast Cancer in the JPEG2000 Domain", 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.", volume = "1", number = "8", pages = "487-4", }
