Automatic Detection and Classification of Microcalcification, Mass, Architectural Distortion and Bilateral Asymmetry in Digital Mammogram
Mammography has been one of the most reliable
methods for early detection of breast cancer. There are different
lesions which are breast cancer characteristic such as
microcalcifications, masses, architectural distortions and bilateral
asymmetry. One of the major challenges of analysing digital
mammogram is how to extract efficient features from it for accurate
cancer classification. In this paper we proposed a hybrid feature
extraction method to detect and classify all four signs of breast
cancer. The proposed method is based on multiscale surrounding
region dependence method, Gabor filters, multi fractal analysis,
directional and morphological analysis. The extracted features are
input to self adaptive resource allocation network (SRAN) classifier
for classification. The validity of our approach is extensively
demonstrated using the two benchmark data sets Mammographic
Image Analysis Society (MIAS) and Digital Database for Screening
Mammograph (DDSM) and the results have been proved to be
progressive.
[1] American Cancer Society, “Glopbal Cancer Facts & Figures”, American
Cancer Society, Atlanda, 2011.
[2] International Agency for Research on Cancer IARC, World Cancer
Report 2008: IARC.
[3] S.Banik, R.M. Rangayyan, and J.E.L. Desautels, “Measures of angular
spread and entropy for the detection of architectural distortion in prior
mammograms”, International Journal of Computer Assisted Radiology
and Surgery, vol. 8, pp. 121–134, 2013.
[4] S. Shanthi and V. Murali Bhaskaran, “Intuitionistic fuzzy c-means and
decision tree approach for breast cancer detection and classification”,
European Journal of Scientific Research, vol. 66, no.3, pp. 345-351,
2011.
[5] S. Shanthi and V. Murali Bhaskaran, “A Novel Approach for Detecting
and Classifying Breast Cancer in Mammogram Images”, International
Journal of Intelligent Information Technologies, vol. 9, no. 1, pp. 21-39,
2013.
[6] S. Shanthi and V. Murali Bhaskaran, “Computer aided system for
detection and classification of breast cancer”, International Journal of
Information Technology, Control and Automation, vol. 2, no.4, pp. 87-
98, 2012.
[7] V. Nguyen, "An Automated Method to Segment and Classify Masses in
Mammograms", World Academy of Science, Engineering and
Technology, vol. 3, no. 4, pp. 761 – 766, 2009.
[8] A. Sindhuja and V. Sadasivam, “Automatic Detection of Breast Tumors
in Sonoelastographic Images Using DWT”, World Academy of Science,
Engineering and Technology, vol. 7, no. 9, pp. 596 – 602, 2013.
[9] S. Banik, R.M. Rangayyan and J.E.L. Desautels, “Detection of
architectural distortion in prior mammograms”, IEEE Transactions on
Medical Imaging, vol. 30, no. 2, pp. 279-294, 2011.
[10] M. Karnan and K. Thangavel, “Automatic detection of the breast border
and nipple positionon digital mammograms using genetic algorithm for
asymmetry approach to detection of microcalcifications”, Computer
Methods and Programs in Biomedicine, vol. 87, pp. 12–20, 2007.
[11] T. Matsubara, T. Ichikawa, T. Hara, H. Fujita, S. Kasai, T. Endo and T.
Iwase, “Automated detection methods for architectural distortions
around skin line and within mammary gland on mammograms”,
International Congress Series, vol. 1256, pp. 950–955, 2003.
[12] A. Oliver, A. Torrent, X. Llado, M. Tortajada, L. Tortajada, M. Sentis, J.
Freixenet, and R. Zwiggelaar, “Automatic microcalcification and cluster
detection for digital and digitised mammograms”, Knowledge-Based
Systems, vol. 28, pp. 68–75, 2012.
[13] R.M. Rangayyan, R.J. Ferrari, and A.F. Frere, “Analysis of bilateral
asymmetry in mammograms using directional, morphological, and
density features”, Journal of Electronic Imaging, vol. 16, no. 1,
pp. 013003-1-013003-12, 2007.
[14] A.N. Karahaliou, I.S. Boniatis, S.G. Skiadopoulos, F.N.
Sakellaropoulos, N.S. Arikidis, E.A. Likaki, G.S. Panayiotakis, and L.I.
Costaridou, “Breast cancer diagnosis: analyzing texture of tissue
surrounding microcalcifications”, IEEE Transactions on Information
Technology in Biomedicine, vol. 12, no. 6, pp. 731– 738,2008.
[15] J.K. Kim, and H.W. Park, “Statistical textural features for detection of
microcalcifications in digitized mammograms”, IEEE Transactions on
Medical Imaging, vol. 18, no. 3, pp. 231-238, 1999.
[16] A. Mencattini, M. Salmeri, G. Rabottino, and S. Salicone, “Metrological
characterization of a CADx system for the classification of breast masses
in mammograms”, IEEE Transactions on Instrumentation and
Measurement, vol. 59, no. 11, pp. 2792–2799, 2010.
[17] A. Tiedeu, C. Daul, A. Kentsop, P. Graebling, and D. Wolf, “Texturebased
analysis of clustered microcalcifications detected on
mammograms”, Digital Signal Processing, vol. 22, no. 1, pp. 124–132,
2012.
[18] S. Shanthi and V. Murali Bhaskaran, “A Novel Approach for
Classification of Abnormalities in Digitized Mammograms”, Sadhana -
Academy Proceedings in Engineering Science journal, vol. 39, no. 5, pp.
1141–1150, 2014.
[19] T. Mu, A.K. Nandi, and R.M. Rangayyan, “Classification of breast
masses using selected shape, edge-sharpness, and texture features with
linear and kernel-based classifiers”, Journal of Digital Imaging,
vol. 21, no. 2, pp. 153-169, 2008.
[20] L de O. Martins, G.B. Junior, A.C. Silva, A.C.D.E. Paiva, and M.
Gattass, “Detection of Masses in Digital Mammograms using K-means
and Support Vector Machine”, Electronic Letters on Computer Vision
and Image Analysis, vol. 8, no. 2, pp. 39-50, 2009.
[21] M. Nemoto, S. Honmura, A. Shimizu, D. Furukawa, H. Kobatake, and
S. Nawano, “A pilot study of architectural distortion detection in
mammograms based on characteristics of line shadows”, International
Journal of Computer Assisted Radiology and Surgery, vol. 4, no. 1,
pp. 27–36, 2009.
[22] R.M. Rangayyan, S.Banik, and J.E.L. Desautels, “Computer-aided
detection of architectural distortion in prior mammograms of interval
cancer”, Journal of Digital Imaging, vol. 23, no. 5, pp. 611-631, 2010.
[23] American College of Radiology, 1998, “Illustrated Breast Imaging
Reporting and Data System BI-RADS”, American College of
Radiology, Reston, PA, 3rd edition, 1998.
[24] R.M. Haralick, K. Shanmugan, and I. Dinstein, “Textural features for
image classification”, IEEE Transactions of System, Man, Cybernetics,
SMC-3, pp. 610–621, 1973.
[25] R. Lopes and N. Betrouni, “Fractal and multifractal analysis: a review”,
Medical Image Analysis, vol. 13, pp. 634–649, 1999.
[26] W. Kinsner, “A unified approach to fractal dimensions”, International
Journal of Cognitive Informatics and Natural Intelligence, vol.1, pp. 26-
46, 2007.
[27] J. Suckling, J. Parker, D.R. Dance, S. Astley, I. Hutt, C. Boggis, I.
Ricketts, E. Stamatakis, N. Cerneaz, SL. Kok, P. Taylor, D. Betal, and
J. Savage, “The Mammographic Image Analysis Society digital
mammogram database”, Proceeding of International Workshop on
Digital Mammography, pp. 211–221,1994.
[28] M. Heath, K. Bowyer, D. Kopans, R. Moor, and W.P. Kegelmeyer,
Proceedings of the Fifth International Workshop on Digital
Mammography, M.J. Yaffe, ed. pp. 212-218, Medical Physics
Publishing, ISBN 1-930524-00-5, 2001.
[1] American Cancer Society, “Glopbal Cancer Facts & Figures”, American
Cancer Society, Atlanda, 2011.
[2] International Agency for Research on Cancer IARC, World Cancer
Report 2008: IARC.
[3] S.Banik, R.M. Rangayyan, and J.E.L. Desautels, “Measures of angular
spread and entropy for the detection of architectural distortion in prior
mammograms”, International Journal of Computer Assisted Radiology
and Surgery, vol. 8, pp. 121–134, 2013.
[4] S. Shanthi and V. Murali Bhaskaran, “Intuitionistic fuzzy c-means and
decision tree approach for breast cancer detection and classification”,
European Journal of Scientific Research, vol. 66, no.3, pp. 345-351,
2011.
[5] S. Shanthi and V. Murali Bhaskaran, “A Novel Approach for Detecting
and Classifying Breast Cancer in Mammogram Images”, International
Journal of Intelligent Information Technologies, vol. 9, no. 1, pp. 21-39,
2013.
[6] S. Shanthi and V. Murali Bhaskaran, “Computer aided system for
detection and classification of breast cancer”, International Journal of
Information Technology, Control and Automation, vol. 2, no.4, pp. 87-
98, 2012.
[7] V. Nguyen, "An Automated Method to Segment and Classify Masses in
Mammograms", World Academy of Science, Engineering and
Technology, vol. 3, no. 4, pp. 761 – 766, 2009.
[8] A. Sindhuja and V. Sadasivam, “Automatic Detection of Breast Tumors
in Sonoelastographic Images Using DWT”, World Academy of Science,
Engineering and Technology, vol. 7, no. 9, pp. 596 – 602, 2013.
[9] S. Banik, R.M. Rangayyan and J.E.L. Desautels, “Detection of
architectural distortion in prior mammograms”, IEEE Transactions on
Medical Imaging, vol. 30, no. 2, pp. 279-294, 2011.
[10] M. Karnan and K. Thangavel, “Automatic detection of the breast border
and nipple positionon digital mammograms using genetic algorithm for
asymmetry approach to detection of microcalcifications”, Computer
Methods and Programs in Biomedicine, vol. 87, pp. 12–20, 2007.
[11] T. Matsubara, T. Ichikawa, T. Hara, H. Fujita, S. Kasai, T. Endo and T.
Iwase, “Automated detection methods for architectural distortions
around skin line and within mammary gland on mammograms”,
International Congress Series, vol. 1256, pp. 950–955, 2003.
[12] A. Oliver, A. Torrent, X. Llado, M. Tortajada, L. Tortajada, M. Sentis, J.
Freixenet, and R. Zwiggelaar, “Automatic microcalcification and cluster
detection for digital and digitised mammograms”, Knowledge-Based
Systems, vol. 28, pp. 68–75, 2012.
[13] R.M. Rangayyan, R.J. Ferrari, and A.F. Frere, “Analysis of bilateral
asymmetry in mammograms using directional, morphological, and
density features”, Journal of Electronic Imaging, vol. 16, no. 1,
pp. 013003-1-013003-12, 2007.
[14] A.N. Karahaliou, I.S. Boniatis, S.G. Skiadopoulos, F.N.
Sakellaropoulos, N.S. Arikidis, E.A. Likaki, G.S. Panayiotakis, and L.I.
Costaridou, “Breast cancer diagnosis: analyzing texture of tissue
surrounding microcalcifications”, IEEE Transactions on Information
Technology in Biomedicine, vol. 12, no. 6, pp. 731– 738,2008.
[15] J.K. Kim, and H.W. Park, “Statistical textural features for detection of
microcalcifications in digitized mammograms”, IEEE Transactions on
Medical Imaging, vol. 18, no. 3, pp. 231-238, 1999.
[16] A. Mencattini, M. Salmeri, G. Rabottino, and S. Salicone, “Metrological
characterization of a CADx system for the classification of breast masses
in mammograms”, IEEE Transactions on Instrumentation and
Measurement, vol. 59, no. 11, pp. 2792–2799, 2010.
[17] A. Tiedeu, C. Daul, A. Kentsop, P. Graebling, and D. Wolf, “Texturebased
analysis of clustered microcalcifications detected on
mammograms”, Digital Signal Processing, vol. 22, no. 1, pp. 124–132,
2012.
[18] S. Shanthi and V. Murali Bhaskaran, “A Novel Approach for
Classification of Abnormalities in Digitized Mammograms”, Sadhana -
Academy Proceedings in Engineering Science journal, vol. 39, no. 5, pp.
1141–1150, 2014.
[19] T. Mu, A.K. Nandi, and R.M. Rangayyan, “Classification of breast
masses using selected shape, edge-sharpness, and texture features with
linear and kernel-based classifiers”, Journal of Digital Imaging,
vol. 21, no. 2, pp. 153-169, 2008.
[20] L de O. Martins, G.B. Junior, A.C. Silva, A.C.D.E. Paiva, and M.
Gattass, “Detection of Masses in Digital Mammograms using K-means
and Support Vector Machine”, Electronic Letters on Computer Vision
and Image Analysis, vol. 8, no. 2, pp. 39-50, 2009.
[21] M. Nemoto, S. Honmura, A. Shimizu, D. Furukawa, H. Kobatake, and
S. Nawano, “A pilot study of architectural distortion detection in
mammograms based on characteristics of line shadows”, International
Journal of Computer Assisted Radiology and Surgery, vol. 4, no. 1,
pp. 27–36, 2009.
[22] R.M. Rangayyan, S.Banik, and J.E.L. Desautels, “Computer-aided
detection of architectural distortion in prior mammograms of interval
cancer”, Journal of Digital Imaging, vol. 23, no. 5, pp. 611-631, 2010.
[23] American College of Radiology, 1998, “Illustrated Breast Imaging
Reporting and Data System BI-RADS”, American College of
Radiology, Reston, PA, 3rd edition, 1998.
[24] R.M. Haralick, K. Shanmugan, and I. Dinstein, “Textural features for
image classification”, IEEE Transactions of System, Man, Cybernetics,
SMC-3, pp. 610–621, 1973.
[25] R. Lopes and N. Betrouni, “Fractal and multifractal analysis: a review”,
Medical Image Analysis, vol. 13, pp. 634–649, 1999.
[26] W. Kinsner, “A unified approach to fractal dimensions”, International
Journal of Cognitive Informatics and Natural Intelligence, vol.1, pp. 26-
46, 2007.
[27] J. Suckling, J. Parker, D.R. Dance, S. Astley, I. Hutt, C. Boggis, I.
Ricketts, E. Stamatakis, N. Cerneaz, SL. Kok, P. Taylor, D. Betal, and
J. Savage, “The Mammographic Image Analysis Society digital
mammogram database”, Proceeding of International Workshop on
Digital Mammography, pp. 211–221,1994.
[28] M. Heath, K. Bowyer, D. Kopans, R. Moor, and W.P. Kegelmeyer,
Proceedings of the Fifth International Workshop on Digital
Mammography, M.J. Yaffe, ed. pp. 212-218, Medical Physics
Publishing, ISBN 1-930524-00-5, 2001.
@article{"International Journal of Medical, Medicine and Health Sciences:68607", author = "S. Shanthi and V. Muralibhaskaran", title = "Automatic Detection and Classification of Microcalcification, Mass, Architectural Distortion and Bilateral Asymmetry in Digital Mammogram", abstract = "Mammography has been one of the most reliable
methods for early detection of breast cancer. There are different
lesions which are breast cancer characteristic such as
microcalcifications, masses, architectural distortions and bilateral
asymmetry. One of the major challenges of analysing digital
mammogram is how to extract efficient features from it for accurate
cancer classification. In this paper we proposed a hybrid feature
extraction method to detect and classify all four signs of breast
cancer. The proposed method is based on multiscale surrounding
region dependence method, Gabor filters, multi fractal analysis,
directional and morphological analysis. The extracted features are
input to self adaptive resource allocation network (SRAN) classifier
for classification. The validity of our approach is extensively
demonstrated using the two benchmark data sets Mammographic
Image Analysis Society (MIAS) and Digital Database for Screening
Mammograph (DDSM) and the results have been proved to be
progressive.
", keywords = "Feature extraction, fractal analysis, Gabor filters,
multiscale surrounding region dependence method, SRAN.", volume = "8", number = "11", pages = "819-6", }
