Mammogram Image Analysis for Breast Cancer Detection

Breast cancer is the uncontrolled growth of cells in the breast region. It is the second leading cause of death in women today. A mammography is an X-ray of the breast tissue. Mammographic image classification can be achieved using Gabor wavelet. The main purpose of the proposed work is to develop a system which classifies mammographic images using Gabor wavelet feature. The images are taken from Mammographic Image Analysis Society (MIAS) database. The proposed system involves three major steps called Pre-processing, Feature Extraction and Classification. Pre-processing reduces noise and normalizes staining intensity. After preprocessing a noise free image goes to the Segmentation phase. Segmentation is the process of partitioning an image into semantically interpretable regions. In feature extraction stage every image is assigned a feature vector to recognize it. Gabor Wavelet is used for Feature Extraction. The extracted features are then dimensionally reduced by Principal Component Analysis (PCA) method to avoid excess computations. Then Support Vector Machine (SVM) classifier is used for classification. The experimental results obtained from the system developed in this research will prove to be beneficial for the automated classification of mammographic images. The proposed method can allow the radiologist to focus rapidly on the relevant parts of the mammogram and it can increase the effectiveness and efficiency of radiology clinics

Texture descriptors applied to digital mammography

Breast cancer is the second cause of death among women cancers. Computer Aided Detection has been demon- strated an useful tool for early diagnosis, a crucial as- pect for a high survival rate. In this context, several re- search works have incorporated texture features in mam- mographic image segmentation and description such as Gray-Level co-occurrence matrices, Local Binary Pat- terns, and many others. This paper presents an approach for breast density classi¯cation based on segmentation and texture feature extraction techniques in order to clas- sify digital mammograms according to their internal tis- sue. The aim of this work is to compare di®erent texture descriptors on the same framework (same algorithms for segmentation and classi¯cation, as well as same images). Extensive results prove the feasibility of the proposed ap- proach.Postprint (published version

A New Computer-Aided Diagnosis System with Modified Genetic Feature Selection for BI-RADS Classification of Breast Masses in Mammograms

Mammography remains the most prevalent imaging tool for early breast cancer screening. The language used to describe abnormalities in mammographic reports is based on the breast Imaging Reporting and Data System (BI-RADS). Assigning a correct BI-RADS category to each examined mammogram is a strenuous and challenging task for even experts. This paper proposes a new and effective computer-aided diagnosis (CAD) system to classify mammographic masses into four assessment categories in BI-RADS. The mass regions are first enhanced by means of histogram equalization and then semiautomatically segmented based on the region growing technique. A total of 130 handcrafted BI-RADS features are then extrcated from the shape, margin, and density of each mass, together with the mass size and the patient's age, as mentioned in BI-RADS mammography. Then, a modified feature selection method based on the genetic algorithm (GA) is proposed to select the most clinically significant BI-RADS features. Finally, a back-propagation neural network (BPN) is employed for classification, and its accuracy is used as the fitness in GA. A set of 500 mammogram images from the digital database of screening mammography (DDSM) is used for evaluation. Our system achieves classification accuracy, positive predictive value, negative predictive value, and Matthews correlation coefficient of 84.5%, 84.4%, 94.8%, and 79.3%, respectively. To our best knowledge, this is the best current result for BI-RADS classification of breast masses in mammography, which makes the proposed system promising to support radiologists for deciding proper patient management based on the automatically assigned BI-RADS categories