Abstract
Feature extraction is an important process for the overall system performance in classification. The objective of this article is to reveal the effectiveness of texture feature analysis for detecting the abnormalities in digitized mammograms using Self Adaptive Resource Allocation Network (SRAN) classifier. Thus, we proposed a feature set based on Gabor filters, fractal analysis, multiscale surrounding region dependence method (MSRDM) to identify the most common appearance of breast cancer namely microcalcification, masses and architectural distortion. The results of the experiments indicate that the proposed features with SRAN classifier can improve the classification performance. The SRAN classifier produces the classification accuracy of 98.44% for the proposed features with 192 images from MIAS dataset.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
American College of Radiology (ACR) 1998 Illustrated breast imaging reporting and data system (BI-RADS). Am. College of Radiology, Reston, VA, 3rd edition
Banik S, Rangayyan R M, and Desautels J E L 2013 Measures of angular spread and entropy for the detection of architectural distortion in prior mammograms. Int. J. Comput. Assist. Radiol. Surg. 8: 121–134
Biswas S K 2011 Recognizing architectural distortion in mammogram: A multiscale Texture Modeling Approach with GMM. IEEE Trans. Biomed. Eng. 58(7): 2023–2030
Chaudhuri B and Sarkar N 1995 Texture segmentation using fractal dimension. IEEE Trans. Pattern Anal. Mach. Intell. 17(1): 72–77
International Agency for Research on Cancer (IARC) 2008 World Cancer Report 2008: IARC
Jemal A, Bray F, Center M M, Ferlay J, Ward E, and Forman D 2011 Global cancer statistics. CA Cancer J. Clin. 61(2): 69–90
Karahaliou A N et al 2008 Breast cancer diagnosis: Analyzing texture of tissue surrounding microcalcifications. IEEE Trans. Inf. Technol. Biomed. 12(6): 731–738
Kuhl C K, Schrading S, Leutner C C, Morakkabati-Spitz N, Wardelmann E, Fimmers R, Kuhn W, and Schild H H 2005 Mammography, breast ultrasound, and magnetic resonance imaging for surveillance of women at high familial risk for breast cancer. J. Clin. Oncol. 23(33): 8469–8476
Lopes R and Betrouni N 2009 Fractal and multifractal analysis: A review. Med. Image Anal. 13: 634–649
Matsubara T, Yamazaki D, Fujita H, Hara T, Iwase T, and Endo T 2000 An automated classification method for mammograms based on evaluation of fibroglandular breast tissue density in Proc. 5th International Workshop on Digital Mammography, Yaffe M J (Ed.) Toronto, Ontario, Canada, 2000, 737–741
Mencattini A, Salmeri M, Rabottino G, and Salicone S 2010 Metrological characterization of a CADx system for the classification of breast masses in mammograms. IEEE Trans. Instrum. Meas. 59(11): 2792–2799
Nemoto M, Honmura S, Shimizu A, Furukawa D, Kobatake H, and Nawano S 2009 A pilot study of architectural distortion detection in mammograms based on characteristics of line shadows. Int. J. Comput. Assist. Radiol. Surg. 4(1): 27–36
Oliver A, Torrent A, Llado X, Tortajada M, Tortajada L, Sentis M, Freixenet J, and Zwiggelaar R 2012 Automatic microcalcification and cluster detection for digital and digitised mammograms. Knowl-Based System 28: 68–75
Paradkar S and Pande S S 2011 Intelligent detection of microcalcification from digitized mammograms. Sadhana - Acad. Proc. Eng. Sci. 36(1): 125–139
Rangayyan R M, Ferrari R J, and Frere A F 2007 Analysis of bilateral asymmetry in mammograms using directional, morphological, and density features. SPIE Proc. J. Electronic Imaging 16(01): 013003-1–013003-12
Shanthi S and Murali Bhaskaran V 2012 Computer aided system for detection and classification of breast cancer. Int. J. Inf. Technol. Control Autom. 2(4): 87–98
Shanthi S and Murali Bhaskaran V 2013 A novel approach for detecting and classifying breast cancer in mammogram images. Int. J. Intell. Inf. Technol. 9(1): 21–39
Suckling J et al 1994 The mammographic image analysis society digital mammogram database. Proc. Int. Workshop Dig. Mammography, 1069: 375–378
Suresh S, Dong K, and Kim H J 2010 A sequential learning algorithm for self-adaptive resource allocation network classifier. Neurocomputing 73(16–18): 3012–3019
Tang J, Rangayyan R M, Xu J, Naqa I E., and Yang Y 2009 Computer-aided detection and diagnosis of breast cancer with mammography: Recent advances. IEEE Trans. Inf. Technol. Biomed. 13 (2): 236–251
Tiedeu A, Daul C, Kentsop A, Graebling P, and Wolf D 2012 Texture-based analysis of clustered microcalcifications detected on mammograms. Digit. Signal Process. 22: 124–132
Wei L, Yang Y, Nishikawa R M, and Jiang Y 2005 A study on several machine-learning methods for classification of malignant and benign clustered microcalcifications. IEEE Trans. Med. Imaging 24(3): 371–380
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
SHANTHI, S., MURALI BHASKARAN, V. A novel approach for classification of abnormalities in digitized mammograms. Sadhana 39, 1141–1150 (2014). https://doi.org/10.1007/s12046-014-0278-x
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12046-014-0278-x