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COMPARATIVE PERFORMANCE EVALUATION OF SEGMENTATION METHODS IN BREAST CANCER IMAGES

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Author(s): BHAGWATI CHARAN PATEL AND SINHA G.R.

Journal: International Journal of Machine Intelligence
ISSN 0975-2927

Volume: 3;
Issue: 3;
Start page: 130;
Date: 2011;
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Keywords: Breast Cancer | Image Segmentation | Image analysis | Contour | Entropy | SNR

ABSTRACT
Breast cancer is one of the major causes of death among women. Segmentation refers to the process of partitioning a digital image into multiple segments (sets of pixels). The goal of segmentation is to simplify and/or change the representation of an image into something that is more meaningful and easier to analyze. Image segmentation is typically used to locate objects and boundaries in images. The result of image segmentation is a set of segments that collectively cover the entire image, or a set of contours extracted from the image. Each of the pixels in a region is similar with respect to some characteristic or computed property, such as color, intensity, or texture. Adjacent regions are significantly different with respect to the same characteristic. We have used various segmentation algorithm methods. In this paper the comparison of the segmented images is done by taking the entropy and SNR information measures and it has been found that the lesion segmentation algorithm closely matches radiologists’ outlines of these lesions.

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