GA Based Neuro Fuzzy Techniques for Breast Cancer Identification Arpita Das 1 and Mahua Bhattacharya 2 1 Institute of Radio Physics & Electronics, University of Calcutta 92, A.P.C. Road, Kolkata-700009, India (e-mail: dasarpita_rpe@yahoo.co.in) 2 Indian Institute of Information Technology & Management, Gwalior Morena Link Road, Gwalior-474010, India (e-mail: mb@iiitm.ac.in) Abstract An intelligent computer-aided diagnostics system may be developed to assist the radiologists to recognize the masses / lesions appearing in breast in different groups of benignancy / malignancy. In present work we have attempted to develop a computer assisted treatment planning system implementing Genetic algorithm based Neuro-fuzzy approaches. The boundary based features of the tumor lesions appearing in breast have been extracted for classification. The shape features represented by Fourier Descriptors, introduce a large number of feature vectors. Thus to classify different boundaries, a standard classifier needs a large number of inputs, and simultaneously to train the classifier a large number of training cycles are required. This may invite the problem of over learning, followed by chance of misclassification. In proposed methodology, Genetic Algorithm (GA) has been used for searching of significant input feature vectors. Finally adaptive neuro fuzzy based classifier has been introduced for classification of tumor masses in breast. 1. Introduction Objective of digital mammography is to detect breast cancer at the early stage of development. Masses appearing in breast are three-dimensional lesions representing sign of breast cancer. Masses are described by their shape, margin and textural characteristics, and may affect the surrounding tissues. The margin is the border, of mass, which is one of the most important criteria to determine whether the mass is belonging to benign group or malignant group. A round or round to oval shape masses with sharply defined borders may have a high likelihood of benign stage. A benign mass generally possesses circumscribed margin. In our earlier work we [1],[2] have suggested shape similarity measure for finding the prognosis of diseases where the idea of shape similarity measure has been implemented by minimization of distance function D between the contours of tumor lesions and the model. In recent years, considerable efforts have been taken to develop automated methods for detection and classification of masses. Many researchers utilized shape descriptors for the detection of microcalcifications [3]-[4]. A comprehensive study of methods using shape descriptors for classification has been reported in [5]. Further in breast cancer identification, development of a fully automated technique for segmentation of tumor masses is a great challenge. Several investigators exploited the methods using intensity values to decide whether a pixel may belong in the region of interest or background [6]. Sahiner et. al. [7] developed an automated, three stages segmentation algorithm including clustering, active contour, and speculation detection stages. Authors [11] have reported methods for discrimination of benign and malignant lesion in breast using ultrasound. In present paper authors have described an improved segmentation process of breast tumor using fuzzy c- means clustering algorithm. To describe the nodular and stellate margin of masses very precisely, authors introduce Fourier descriptors as shape representing features [8]. In the next stage, classifier has been designed using adaptive neuro fuzzy techniques [9] to discriminate the benignancy from malignant growth of tumor. This method also incorporates the automated false positive reduction of mass boundaries. 2. Morphology of Tumor Mass Masses in mammograms are compact areas that appear brighter than the tissue in which they are embedded because of higher attenuation of X-rays. The primary features that indicate malignancy are related to the mass shape and margin. Fig-1 illustrates the morphological spectrum of breast masses frequently seen on mammograms [10]. International Machine Vision and Image Processing Conference 978-0-7695-3332-2/08 $25.00 © 2008 IEEE DOI 10.1109/IMVIP.2008.19 136