Bmd Method

Abstract

Breast mass segmentation in mammography plays a crucial role in Computer-Aided Diagnosis (CAD) systems. In this paper a Bidimensional Empirical Mode Decomposition (BEMD) method is introduced for the mass segmentation in mammography images. This method is used to decompose images into a set of functions named Bidimensional Intrinsic Mode Functions (BIMF) and a residue. Our approach consists of three steps: 1) the regions of interest (ROIs) were identified by using iterative thresholding; 2) the contour of the regions of interest (ROI) was extracted from the first BIMF by using the (BEMD) method; 3) the region of interest was finally refined by the extracted contour. The proposed approach is tested on (MIAS) database and the obtained

It is also one of the leading causes of cancer death. The statistics show that breast cancer affects one of every eight women in the United States and one of every ten women in Europe [1]. Women can have the highest chance of survival if physicians are able to detect the cancer at its early stages.

One of the leading methods for diagnosing breast cancer is screening mammography. This method involves X-ray imaging of the breast. Screening mammography examinations are performed on asymptomatic women to detect early, clinically unsuspected breast cancer [2]. The need for early detection of breast cancer is highlighted by the fact that incidence rates for breast cancer is one of the highest among all cancers according to the American Cancer Society which quotes a morbidity of 230 000 and a mortality of 40 000 according to the latest figures gathered for the American population [6].

Thus, early diagnosis plays a critical role in increasing the chance of survival. Therefore, segmentation of breast mass in the mammography computer aided diagnosis (CAD) plays an important role in the quantitative and qualitative analysis of medical images. It has a direct impact on the analysis and treatment of early breast

Anitha et al., discussed mass detection and classification in mammogram images with the use of features extracted from the mass regions obtained by the automatic morphological based segmentation method [18]. In this approach, the wavelet features were extracted from the detected mass regions and were compared with features extracted using Gray Level Co-occurrence Matrix (GLCM) to differentiate the TP and FP regions. Classifications of the mass regions were carried out through the Support Vector Machine (SVM) to separate the segmented regions into masses and non-masses based on the features. Herwanto et al., applied association technique based on classification algorithm to classify microcalcification and mass in