Mammography is used to aid early detection and diagnosis systems. It takes an x-ray\ud image of the breast and can provide a second opinion for radiologists. The earlier\ud detection is made, the better treatment works. Digital mammograms are dealt with by\ud Computer Aided Diagnosis (CAD) systems that can detect and analyze abnormalities in\ud a mammogram. The purpose of this study is to investigate how to categories cropped\ud regions of interest (ROI) from digital mammogram images into two classes; normal and\ud abnormal regions (which contain microcalcifications).\ud The work proposed in this thesis is divided into three stages to provide a concept\ud system for classification between normal and abnormal cases. The first stage is the\ud Segmentation Process, which applies thresholding filters to separate the abnormal\ud objects (foreground) from the breast tissue (background). Moreover, this study has been\ud carried out on mammogram images and mainly on cropped ROI images from different\ud sizes that represent individual microcalcification and ROI that represent a cluster of\ud microcalcifications. The second stage in this thesis is feature extraction. This stage\ud makes use of the segmented ROI images to extract characteristic features that would\ud help in identifying regions of interest. The wavelet transform has been utilized for this\ud process as it provides a variety of features that could be examined in future studies. The\ud third and final stage is classification, where machine learning is applied to be able to\ud distinguish between normal ROI images and ROI images that may contain\ud microcalcifications. The result indicated was that by combining wavelet transform and\ud SVM we can distinguish between regions with normal breast tissue and regions that\ud include microcalcifications
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