Mammography In Breast Cancer

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2.1 Introduction
Mammography plays a vital role in early detection of breast cancers because it can show abnormalities in the breast up to two years before a patient or physician can feel them. The digital mammogram is analyzed with a combination of general image processing and computer vision algorithms combined with procedures which have been specially designed for the application. Mammography has provided reliable parameters for detecting breast tumor. Masses and calcifications are the most general breast deformities that may specify the occurrence of breast cancer. The other symptoms of breast cancer are architectural distortion and bilateral asymmetry, etc., Breast abnormalities are defined by an extensive range of features and may be easily overlooked or wrongly interpreted by radiologists while appraising large number of mammography images made available in screening programs. CAD and Computer Aided Diagnosis (CADX) tools are being designed to help the radiologists for providing an accurate diagnosis. CAD and CADX algorithms lend a hand in reducing the number of FPs and they help radiologists to make better decisions. This chapter gives a review of image processing algorithms that have been developed for detection of breast cancer.
Breast cancer is the top cancer in women both in the developed and the developing countries [133] and it is one of the major reasons for the increase in mortality among middle aged women especially in developed countries [58]. If it is detected and diagnosed in early stages of development, it will increase the possibility of successful treatment and chances for complete recovery of the patient. Mammography screening programs have reduced mortality rates by 30-70% [111]. In mammography images, ...

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...io lateral oblique (MLO)) of the same breast, and same view mammograms were taken at different times. Unsupervised segmentation using a single view can in turn be categorized into six classes, region-based segmentation, contour-based segmentation, clustering segmentation, pseudo-color segmentation, graph segmentation, and variant-feature transformation.
2.3.1 Breast Region Segmentation
Breast segmentation techniques set the focus of the search for abnormalities on the region of the breast excluding its background. The techniques used for segmenting are similar to those used in the regions of interest segmentation and can also be categorized with the same perspectives. As an example of approaches that can be listed under the clustering segmentation method is the novel approach proposed by Shahedi et al., [80] for breast region segmentation based on local threshold.

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