Abstract
In this paper, we present an efficient computer-aided mass classification method in digitized mammograms using Association rule mining, which performs benign–malignant classification on region of interest that contains mass. One of the major mammographic characteristics for mass classification is texture. Association rule mining (ARM) exploits this important factor to classify the mass into benign or malignant. The statistical textural features used in characterizing the masses are mean, standard deviation, entropy, skewness, kurtosis and uniformity. The main aim of the method is to increase the effectiveness and efficiency of the classification process in an objective manner to reduce the numbers of false-positive of malignancies. Correlated association rule mining was proposed for classifying the marked regions into benign and malignant and 98.6% sensitivity and 97.4% specificity is achieved that is very much promising compare to the radiologist’s sensitivity 75%.
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Oliver A, Freixenet J, Marti R, Pont J, Perez E, Denton ERE, Zwiggelaar R (2008) A novel breast tissue density classification methodology. IEEE Trans Inf Technol Biomed 12(1):55–65
Bird RE (1990) Professional quality assurance for mammography screening programs. J Radiol 175:587–605
Cheng HD, Shi XJ, Min R, Hu LM, Cai XP, Du HN (2006) Approaches for automated detection and classification of masses in mammograms. Pattern Recogn 39:646–668
Yang SC, Wany CM et al (2005) A computer-aided system for mass detection and classification indigitized mammograms. J Biomed Eng Appl Basis Commun 17:215–228
Duda RO, Hart PE, Stork DG (2001) Pattern classification, 2nd edn. Wiley, New York
Chan HP, Wei D, Helvie MA, Sahiner B et al (1995) Computer-aided classification of mammographic masses and normal tissue: linear discriminant analysis in texture feature space. J Phys Med Biol 40:857–876
Sahiner B, Petrick N, Chan HP (2001) Computer-aided characterization of mammographic masses: accuracy of mass segmentation and its effects on characterization. IEEE Trans Med Imaging 20(12):1275–1284
Viton JL, Rasigni M, Rasigni G, Liebaria A (1996) Method for characterizing masses in digital mammograms. Opt Eng 35(12):3453–3459
Lisboa PJG (2000) A review of evidence of health benefits from artificial neural networks in medical intervention. Neural Netw 15:11–39
Alginahi Y (2004) Computer analysis of composite documents with non-uniform background. PhD thesis, Electrical and Computer Engineering, University of Windsor, Windsor
Alginahi Y (2008) Thresholding and character recognition in security documents with watermarked background. In: Proceedings of the international conference on digital image computing: techniques and applications, pp 220–225
Zheng B, Chang YH, Wang XH, Good WF (1999) Comparison of artificial neural network and Bayesian belief network in a computer assisted diagnosis scheme for mammography. In: IEEE international conference on neural networks, pp 4181–4185
Sahiner B, Chan HP, Petrick N, Helvie MA, Goodsitt MM (1998) Design of a high-sensitivity classifier based on a genetic algorithm: application to computer-aided diagnosis. Phys Med Biol 43(10):2853–2871
Agrawal R, Imielinski T, Swami AN (1993) Mining association rules between sets of items in large databases. In: Proceedings of the 1993 ACM SIGMOD ICMD. ACM, Washington, DC, pp 207–216
Ribeiro MX, Traina AJM, Balan AGR, Traina C, Jr, Marques PMA (2007) SuGAR: a framework to support mammogram diagnosis. In: IEEE CBMS 2007. Maribor, Slovenia, pp 47–52
Yun J, Zhanhuai L, Yong W, Longbo Z (2005) Joining associative classifier for medical images. HIS, pp 367–372
Tseng SV, Wang M-H, Su J-H (2005) A new method for image classification by using multilevel association rules. Presented at ICDE 05; Tokyo, pp 1180–1187
Zimmermann A, De Raedt L (2004) CorClass: Correlated association rule mining for classification. Proc Discov Sci 3245:60–72
Alginahi Y, Sid-Ahmed MA, Ahmadi M (2004) Local thresholding of composite documents using multi-layer perception neural network. In: 47th IEEE international midwest symposium on circuits and systems, pp 209–212
Sid-Ahmed MA (1995) Image processing theory, algorithms and architectures, 1st edn. McGraw-Hill, New York. Int J Artif Intell Appl 1(3) July 2010
Wang X, Smith MR, Rangayyan RM (2004) Mammographic information analysis through association-rule mining. Canadian conference on electrical and computer engineering 2004. doi:10.1109/CCECE.2004.1349689
Roselin R, Thangavel K (2010) Classification ensemble for mammograms using ant-miner. International conference on computing communication and networking technologies (ICCCNT), pp 1–6. doi:10.1109/ICCCNT.2010.5592607
Dua S, Singh H, Thompson HW (2009) Associative classification of mammograms using weighted rules. Expert Syst Appl 36(5):9250–9259. doi:10.1016/j.eswa.2008.12.050
Rajendran P, Madheswaran M (2010) Novel fuzzy association rule image mining algorithm for medical Decision support system. Int J Comput Appl 1(20):87–94. doi:10.5120/415-613
Kobyliński L, Walczak K Image classification with customized associative classifiers. In: Proceedings of the international multiconference on computer science and information technology, pp 85–91
Lairenjam B, Wasan SK (2009) Neural network with classification based on multiple association rule for classifying mammographic data. In: Proceedings of IDEAL’2009, pp 465–476
Thangavel K, Kaja Mohideen A (2009) Classification of microcalcifications using multi-dimensional genetic association rule miner. Int J Recent Trends Eng 2(2):233–235
Holmes G, Donkin A, Witten IH (1994) WEKA: a machine learning workbench. In: Proceedings second Australia and New Zealand conference on intelligent information systems, Brisbane, pp 357–361
Veldkamp WJH, Karssemeijer N, Otten JDM, Hendriks JHCL (2000) Automated classification of clustered microcalcifications into malignant and benign types. Med Phys 27(11):2600–2608
Wei L, Yang Y, Nishikawa RM, Jiang Y (2005) A study on several machine-learning methods for classification of malignant and benign clustered microcalcifications. IEEE Trans Med Imaging 24(2):371–380
Abu-Amara F, Abdel-Qader I (2009) Hybrid mammogram classification using rough set and fuzzy classifier. Int J Biomed Imaging 2009. doi:10.1155/2009/680508
Chan H-P, Sahiner B, Patrick N, Helvie MA, Lam KL, Adler DD, Goodsitt MM (1997) Computerized classification of malignant and benign microcalcifications on mammograms: texture analysis using an artificial neural network. Phys Med Biol 42(3):549–567
Vibha L, Harshavardhan GM, Pranaw K, Deepa Shenoy P, Venugopal KR, Patnaik LM Classification of mammograms using decision trees, database engineering and applications symposium, IDEAS’06″. doi:10.1109/IDEAS.2006.14
Thangavel K, Roselin R (2009) Mammogram mining with genetic optimization of ant-miner parameters. Int J Recent Trends Eng 2(3):67–69
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An erratum to this article is available at http://dx.doi.org/10.1007/s00521-015-2084-8.
The Editor-in-Chief has decided to retract this article. Upon investigation carried out according to the Committee on Publication Ethics guidelines, it has been found that the authors have duplicated substantial parts from the following article: Classification Using Association Rules Rajanish Dass W.P. No. 2008-01-05 January 2008 INDIAN INSTITUTE OF MANAGEMENT AHMEDABAD INDIA
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Mohanty, A.K., Senapati, M., Beberta, S. et al. RETRACTED ARTICLE: Mass classification method in mammograms using correlated association rule mining. Neural Comput & Applic 23, 273–281 (2013). https://doi.org/10.1007/s00521-012-0857-x
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DOI: https://doi.org/10.1007/s00521-012-0857-x