English

Abstract

An intelligent computer aided diagnosis system can be very helpful for radiologist in detecting and diagnosing breast cancer faster than typical screening program. This study attempted to segment the masses accurately and distinguish malignant from benign masses. The suspicious location of the breast masses are specified by the radiologists and then masses are accurately segmented using fuzzy c-means clustering technique. Fourier descriptors are utilized for the extraction of shape features of mammographic masses. These shape features along with the texture features are fed to the input of the ANFIS classifier for determination of the masses as benign, lobular or malignant. The classification system utilizes a simple Euclidian distance metric to determine the degree of malignancy. The study involves 40 digitized mammograms from MIAS, BIRADS database and has to be found 87% correct classification rate. Keyword: Breast Cancer Diagnosis system, Breast Masses, Fourier Descriptors, Textural Descriptors, Fuzzy c-means Clustering, ANFIS model. Introduction Breast cancer is one of the leading causes of death for women. Women have better chance to survive if breast cancer can be detected early. XRay mammography is the most important modality, which is used for early detection of breast cancer [1, 3, 6, 7, 10, 13, 15, 19]. The failure to detect any abnormal lesion at an early stage may lead to disastrous consequences. The mammographic images show signs of obstruction and many direct and indirect radiographic signs due to space occupying lesions in the tissue region of breast [1-3]. The improvement of mammographic image quality is essential for breast cancer screening. Therefore need exists to automate the process of analyzing a large number of mammograms and to discriminate the benign lesion from malignant one for proper therapy planning. The interpretation of mammograms by radiologists is done by visual examination of films to find out the presence of abnormalities. The computer aided diagnostics and detection procedures of masses as well as tissues are depicted by researchers in [4, 5, 15, 20, 26, 28, 29] using digital mammogram and other modalities. The present work is continuation of our earlier work based on theory of shape [12-14] related to gradation of benignancy of tumor mass in tissue region. The concept of symmetry analysis of shape computes a distance function D between the contour of the tumor model and the contour of the pattern tumor lesion. The distance function D has been further utilized to classify the tumors mainly in two broad categories benign and malignant transformations. The shape of a contour was described on the basis of its structural features using chain code representation. The biological characteristics of tumor mass have been stated in [11] that the low grade homogeneous benign glioma is usually smooth masses with single radius whereas malignant transformation has multiple protrusions. We have suggested [12-14] earlier shape similarity measure μ to find out 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. These measurements give the indication of benignancy or malignancy of tumor lesions from a conventional coarse grading to a finer grading. In recent years, considerable efforts have been taken to develop automated methods for detection and classification of mammographic masses [7-10, 15-16, [18-23, 25, 27-28]. Brzakovic et al. investigated an expert system for analysis of mammogram [15]. Bruce et. al. [17] reported the results of applying multiresolution techniques to the problem of tumor mass classification. They utilized discrete wavelet transform modulus-maxima method for the extraction of mammographic mass shape features. In many breast cancer diagnostic systems fully automated techniques for mass segmentation is a major challenge. Several investigators exploited methods using intensity values to decide whether a pixel may belong in the region of interest (ROI) or background [1819]. Mudigonda et al. [20] proposed an algorithm to segment masses by establishing intensity links, and to analyze oriented flow-like textural information in the ribbons of pixels across the margins of masses to determine if the segmented regions are true mass regions or false positives. Li et al. [21] developed a segmentation method that uses probability to determine segmented contours. Sahiner et. al. [22] developed an automated, three-stage segmentation method including clustering, active contour, and speculation detection stages. The method has Soft Computing Based Decision Making Approach for Tumor Mass Identification in Mammogram International Journal of Bioinformatics Research, ISSN: 0975–3087, Volume 1, Issue 2, 2009 38 been used for classification of the segmented masses as benign or malignant using speculation measures and morphological features. The approach based on support vector machine has been reported in [24] for detection of microcalcification in breast. In present paper authors have described an improved segmentation process of tumor mass using Fuzzy c-means clustering algorithm. In the next stage classifier has been designed using adaptive neuro fuzzy techniques [23, 25, 27] to discriminate the benignancy from malignancy of growth of tumor lesion in breast identified by mammograms. This method also incorporates the automated false positive reduction of mass boundaries. In this article, to describe the margins of tumor masses very precisely, we introduce Fourier descriptors [25] as shapebased features. To achieve high level of accuracy in malignancy detection, we combine statistical texture features of suspicious region in conjunction with shape-based features. Proposed Method for Classification of Tumor Masses Masses in mammograms are compact areas that appear brighter than the tissue in which they are embedded because of higher attenuation of Xrays. When the tissue surrounding a mass is fatty, detection is relatively easy. However, when a mass is buried in dense tissue, it may be very difficult to identify. The primary features that indicate malignancy are related to the mass size, shape, borders and texture. In present paper we have proposed techniques based on neuro fuzzy softcomputing to classify the tumor masses appearing in breast in different groups of benignancy and malignancy. Shape based boundary features and texture features have been extracted from region of interest ROI of tumor mass and which are fed to the classifier developed using adaptive neuro fuzzy based techniques. The classifier takes the decision, whether the masses are benign or malignant. The features extracted from shape and texture information provide much more accurate decision on benignancy/malignancy of masses than a single feature. The overview of the proposed method is presented below in Fig. (1). Segmentation of Tumor Mass Using Fuzzy cMeans Clustering Algorithm Segmentation of tumor mass is an important prior step for further classification/ identification. The radiologists may be confused due to the presence of high-contrast fibroglandular [29-30] tissue in the mammograms as the actual calcified masses. In this regard our objective is to develop a robust technique for segmentation of calcified masses from breast tissue. Presently fuzzy cmeans clustering algorithm has been used for intensity based segmentation of masses. Total number of fuzzy cluster centers chosen is three as shown in Fig. (2). Cluster center A represents the healthy breast tissue. Second cluster B represents false presence of mass region and C represents actual mass region. Three cluster centers are selected to segment the tiny calcifications/tumors of mammograms in presence of different types of tissues and muscles. The accuracy of final classification of tumors in different grades of benignancy / malignancy depends on the superiority of segmentation process. In the proposed method, fuzzy c-means clustering algorithm has been used for intensity based segmentation of microcalcification clusters. Consulting with radiologists and after a detailed discussion with them it can be concluded that choice of three fuzzy cluster centers (as shown in Fig. 2) satisfy our requirements i.e. suppress the high FP rate and preserve the true segmentation details of fine calcification spots. In Fig. (2), cluster A represents the normal breast tissues. Second cluster B represents the false presence of microcalcifications and C represents the actual calcification points. The segmentation results also justify our assumption. The ultimate Fuzzy partition membership functions have been shown in Fig. 2, which shows that there is an overlapping between the membership functions A, B & C. If the possibility of belongingness of any region of breast to the calcification part is greater than 50% i.e. the membership value of curve C > 0.5 (hashed portion of curve C), decision may be taken that the particular region is within the calcified lesion. According to the decision rule, the shaded region in Fig. 2 indicates the Region of interest (ROI). Algorithm: Let X={x1, x2,........, x n} be a set of given data. A fuzzy c-partition of X is a family of fuzzy subsets of X, denotes by P = {A1, A2,........, Ac}, which satisfies (1) The performance index of a fuzzy partition P, Jm (Ρ), is defined in terms of the cluster centers by