Breast cancer is considered as one of a major health problem that constitutes the strongest cause behind mortality among women in the world. So, in this decade, breast cancer is the second most common type of cancer, in term of appearance frequency, and the fifth most common cause of cancer related death. In order to reduce the workload on radiologists, a variety of CAD systems; Computer-Aided Diagnosis (CADi) and Computer-Aided Detection (CADe) have been proposed. In this paper, we interested on CADe tool to help radiologist to detect cancer. The proposed CADe is based on a three-step work flow; namely, detection, analysis and classification. This paper deals with the problem of automatic detection of Region Of Interest (ROI) based on Level Set approach depended on edge and region criteria. This approach gives good visual information from the radiologist. After that, the features extraction using textures characteristics and the vector classification using Multilayer Perception (MLP) and k-Nearest Neighbours (KNN) are adopted to distinguish different ACR (American College of Radiology) classification. Moreover, we use the Digital Database for Screening Mammography (DDSM) for experiments and these results in term of accuracy varied between 60 % and 70% are acceptable and must be ameliorated to aid radiologist.
Screening mammography represents the technique adopted to detect breast cancer at an early stage. However the presence of artifacts and pectoral muscle can disturb the detection of breast cancer and reduce the rate of accuracy in the computer aided analysis (CAD). For this reason, the preprocessing of mammogram images is very important in the process of breast cancer analysis because it reduces the number of false positive. It also allows radiologists to help in the comparison between mammograms. The aim of this paper is to propose a method of pre-processing on medio-lateral oblique-view (MLO) mammograms that is composed of two stages: the first step helps to extract the breast region from the rest of the image (background.), while the second aims at the suppression of the pectoral muscle. To extract the breast region, we used a method based on automatic thresholding (Otsu's) and Connected Component Labelling algorithm. Identifying the pectoral muscle has been done using the Hough transform and active contour. We evaluated our pre-processing method on a set of 80 images obtained from the DDSM database and we found that breast region extraction gave an excellent success rate that reached 100%. The success rate in the removal of the pectoral muscle was 92.5% with the use of Hough transform and active contour.
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