Cover page and Table of Contents: PDF (size: 851KB)
Full Text (PDF, 851KB), PP.66-74
Views: 0 Downloads: 0
Mammogram, ROI, GLCM, PreARM, Apriori algorithm, ESAR
Among women, 12% possibility of developing a breast cancer and 3.5% possibility of mortality due to this cause is reported . Nowadays early detection of breast cancer became very important. Mammogram - a breast X-ray is used to investigate and diagnose breast cancer. In this paper, authors propose GLCM (Grey Level Co-occurrence Matrix) feature based improved mammogram classification using an associative classifier. Mining of association rules from mammogram dataset discovers frequently occurring patterns. It depends on user specified minimum confidence and support value. This dependency causes an increase in search space. The authors propose two-phase optimization procedure to overcome these limitations.
The initial phase comprises feature optimization by adopting proposed PreARM (Pre-processing step for Association Rule Mining) method. The next phase comprises association rule optimization by adopting proposed ESAR (Extraction of Strong Association Rules) method to generate efficient, highly correlated and robust rules. Proposed associative classification method is substantiated by adapting authentic MIAS and DDSM mammogram database. The experimentation concedes 91% and 90% trimming of GLCM features and association rules by adopting PreARM and ESAR algorithms respectively. Using optimized association rules, the classification accuracies procured for MIAS and DDSM datasets are 92% and 94% respectively. Area under Receiver Operating Characteristic (ROC) curves obtained by proposed system for MIAS and DDSM datasets are 0.9656 and 0.9285 respectively. Results of GLCM based associative classifier are compared with GLCM based Random Forest (RF), an ensemble learning method. The experimental result shows that GLCM based associative classifier outperforms RF method with respect to accuracy and AUC, and it is a promising method for mammogram classification.
Jyoti Deshmukh, Udhav Bhosle,"GLCM based Improved Mammogram Classification using Associative Classifier", International Journal of Image, Graphics and Signal Processing(IJIGSP), Vol.9, No.7, pp.66-74, 2017. DOI: 10.5815/ijigsp.2017.07.07
J. Tang, R.M. Rangayyan, J. Xu, I. El Naqa and Y. Yang, “Computer-aided detection and diagnosis of breast cancer with mammography: recent advances”, IEEE Transactions on Information Technology in Biomedicine, vol. 13.2, pp.236-251, 2009.
H.D. Nelson, K. Tyne, A. Naik, C. Bougatsos, B.K. Chan and L. Humphrey, “Screening for breast cancer: an update for the US Preventive Services Task Force”, Annals of internal medicine, vol. 151.10, pp.727-737, 2009.
H.P. Chan, B. Sahiner, M.A. Helvie, N. Petrick, M.A. Roubidoux, T.E. Wilson, et al., “Improvement of Radiologists' Characterization of Mammographic Masses by Using Computer-aided Diagnosis: An ROC Study 1”, Radiology, vol. 212.3, pp.817-827, 1999.
C. Ordonez and E. Omiecinski, “Discovering association rules based on image content”, In Research and Technology Advances in Digital Libraries, 1999. Proceedings. IEEE Forum on, pp. 38-49, IEEE, 1999.
R. Agrawal, T. Imieliński, and A. Swami, "Mining association rules between sets of items in large databases", ACM SIGMOD Record, vol. 22.2, pp. 207-216, 1993.
R. Ramani, N.Suthanthira Vanitha, S. Valarmathy, "The Pre-Processing Techniques for Breast Cancer Detection in Mammography Images", IJIGSP, vol.5, no.5, pp.47-54, 2013.DOI: 10.5815/ijigsp.2013.05.06.
J. Nagi, S.A. Kareem, F. Nagi and S.K. Ahmed, “Automated breast profile segmentation for ROI detection using digital mammograms”, In Biomedical Engineering and Sciences (IECBES), 2010 IEEE EMBS Conference on, pp. 87-92, IEEE, November 2010.
K. Beyer, J. Goldstein, R. Ramakrishnan, U. Shaft, "When is ‘nearest neighbor’ meaningful?”, In Database theory—ICDT’99, Springer Berlin Heidelberg, pp. 217-235, 1999.
R.M. Haralick and K. Shanmugam, “Textural features for image classification”, IEEE Transactions on systems, man, and cybernetics, vol. 3.6, pp.610-621, 1973.
M. X. Ribeiro, C. Traina, and P. M. Azevedo-Marques, "An association rule-based method to support medical image diagnosis with efficiency", IEEE Transactions on Multimedia, vol. 10.2, pp. 277-285, 2008.
M. L.Antonie, O. R. Zaiane, and A. Coman, "Application of data mining techniques for medical image classification", MDM/KDD2001, pp. 94-101, 2001.
M. L. Antonie, O. R. Zaiane, and A. Coman, "Associative classifiers for medical images", Mining Multimedia and Complex Data, Springer Berlin Heidelberg, pp. 68-83, 2002.
O. R. Zaıane, M. L. Antonie, and A. Coman, "Mammography classification by an association rule-based classifier", MDM/KDD, pp. 62-69, 2002.
J. Yun, L. Zhanhuai, W. Yong, Z. Longbo, "Joining associative classifier for medical images", In Fifth International Conference on Hybrid Intelligent Systems (HIS'05), IEEE, 2005.
S. Dua, H. Singh, and H. W. Thompson, "Associative classification of mammograms using weighted rules", Expert systems with applications, vol. 36.5, pp. 9250-9259, 2009.
J. Deshmukh and U. Bhosle, "SIFT with associative classifier for mammogram classification", In Signal and Information Processing (ICONSIP), International Conference on, pages 1-5, IEEE, 2016
J. Han, J. Pei, and M. Kamber, Data mining: concepts and techniques, Elsevier, 2011.
J. Deshmukh and U. Bhosle, "Image mining using association rule for medical image dataset", Procedia Computer Science, vol. 85, pp. 117-124, 2016.
L. Breiman, "Random forests", Machine learning, vol. 45.1, pp. 5-32, 2001.
R. Ramani, N. Suthanthira Vanitha, "Computer Aided Detection of Tumours in Mammograms", IJIGSP, vol.6, no.4, pp.54-59, 2014.DOI: 10.5815/ijigsp.2014.04.07
J. Suckling et al., "The mammographic image analysis society digital mammogram database", Exerpta Medica. International Congress Series, Vol. 1069, pp. 375-378, 1994.
T. M. Deserno, M. Soiron, and J. E. de Oliveir, "Texture patterns extracted from digitizes mammograms of different BI-RADS classes", Image Retrieval in Medical Applications Project, release, 1, 2012.