Journal Title : International Journal of Modern Trends in Engineering and Science
Author’s Name : G.G.Udaya, J.Preethi
Volume 01 Issue o6 Year 2014
ISSN no: 2348-3121
Page no: 1-5
Abstract— Breast cancer is the second leading cause of mortality among women. Over the past 50 years, it has become a major health issue in the world and its rate has increased in recent years. Early detection is the most effective way to diagnose and handle breast cancer. Computer-aided detection or diagnosis (CAD) systems can play a key role in the early detection of breast cancer and can reduce the death rate among women with breast cancer. The CAD system consists of preprocessing, detection and classification of microcalcification. Preprocessing of mammograms is done to improve the quality of the image and in separating the pectoral muscle from the whole breast area. Detection of microcalcification is done by segmentation. In this Particle Swarm Optimization (PSO) has been proposed to segment the suspicious regions from the enhanced image. Classification is done by three steps namely feature extraction, feature selection and by classification. Support Vector Machine (SVM) is used as a classifier, which classifies the microcalcification into benign and malignant. SVM improves classification accuracy and proved to possess the best recognition ability due to its ability to deal with nonlinearly separable data sets.
Keywords— Breast cancer, mammogram, pectoral muscle suppression, computer aided detection, microcalcification, segmentation.
 Karthikeyan Ganesan, U. Rajendra Acharya,Chua Kuang Chua, Lim Choo Min, K. Thomas Abraham, and Kwan-Hoong Ng, “Computer-Aided Breast Cancer Detection Using Mammograms: A Review”, IEEE Reviews In Biomedical Engineering, Vol. 6, 2013 77.
 Vs, M. F. Akay, “Support vector machines combined with feature selection for breast cancer diagnosis,” Expert Syst. With Applicat., Vol. 36, No. 2, pt. 2, pp. 3240–3247, Mar. (2009), 10.1016/j.eswa.2008.01.009, 0957-4174.
 R. M. Haralick, “Textural features for image classification,” IEEE Trans. Syst., Man, Cybern., Vol. 3, pp. 610–621, Dec. (1973).
 N. Karssemeijer, “Adaptive noise equalization and recognition of microcalcification clusters in mammograms,” Int. J. Pattern Recog. Artif.Intell., Vol. 7, pp. 1357–1376, (1993).
 C. J. Vyborny, M. L. Giger, and R. M. Nishikawa, “Computer aided detection and diagnosis of breast cancer,” Radiologic Clinics N. Amer., Vol. 38, No. 4, pp. 725–740, Jul. 1, (2000), 10.1016/S0033-8389(05)70197-4, 0033-8389.
 K. J. McLoughlin, P. J. Bones, and N.Karssemeijer, “Noise equalization for detection of microcalcification clusters in direct digital mammogram images,” IEEE Trans. Med. Imag., Vol. 23, No. 3, pp. 313–320, Mar. 2004, 10.1109/TMI.2004.824240.
 G. Boccignone, A. Chianese, and A. Picariello, “Computer aided detection of microcalcifications in digital mammograms,” Computers Biol. Medicine, Vol. 30, No. 5, pp. 267–286, Sep. 1, (2000), 10.1016/S0010-4825(00)00014-7, 0010-4825.
 A. Papadopoulos, D. I. Fotiadis, and L. Costaridou, “Improvement of microcalcification cluster detection in mammography utilizing image enhancement techniques,” Computers Biol. Medicine, Vol.38, no. 10, pp. 1045–1055, Oct. (2008),10.1016/j.compbiomed.2008.07.006, 0010-4825.
 A. Laine, J. Fan, and W. Yang, “Wavelets for contrast enhancement of digital mammography,” IEEE Eng. Medicine Biol., Vol. 14, No. 5, pp. 536–550, Sep./Oct. (1995), 10.1109/51.464770.
H. Jing, Y. Yang, and R. M. Nishikawa, “Detection of clustered microcalcifications using spatial point process modeling,” Phys.Med. Biol., Vol. 56, No. 1, 2011, 10.1088/0031-9155/56/1/001.
H. Kobatake, M. Murakami, H. Takeo, and S. Nawano, “Computerized detection of malignant tumors on digital mammograms,” IEEE Trans. Med. Imag., Vol. 18, No. 5, pp. 369–378, May 1999, 10.1109/42. 774164.
Guliato, R. M. Rangayyan, W. A. Carnielli, J. A. Zuffo, and J. E. L. Desautels, “Segmentation of breast tumors in mammograms by fuzzy region growing,” in Proc. 20th Annu. Int. Conf. IEEE Engineering Medicine Biology Soc., Hong Kong, Oct. 29–Nov. 1 1998, pp. II:1002–II:1004.
F. F. Yin, M. L. Giger, K. Doi, C. J. Vyborny, and R. A. Schmidt, “Computerized detection of masses in digital mammograms: Automated alignment of breast images and its effect on bilateral-subtraction technique,” Med. Phys., Vol. 21, No. 3, pp. 445–452, (1994).
A. J. Mendez, P. G. Tahoces, M. J. Lado, M. Souto, and J. J. Vidal, “Computer-aided diagnosis: Automatic detection of malignant masses in digitized mammograms,” Med. Phys., Vol. 25, No. 6, pp. 957–964, (1998).
M. Kato, H. Fujita, T. Hara, and T. Endo, “Improvement of automated breast-region-extraction algorithm in a mammogram CAD system,” Med. Imag. Inform. Sci., Vol. 14, No. 2, pp. 104–113, (1997).