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Color Image Segmentation of Endoscopic and Microscopic Images for Abnormality Detection in Esophagus
Abstract
The study of medical image analysis encompasses the various techniques for acquisition of images of biological structures pertaining to human body using radiations in different frequency ranges. The advancements in medical imaging over the past decades are enabling physicians to non-invasively peer inside the human body for the purpose of diagnosis and therapy. In this chapter, the objective is to focus on the studies relating to the analysis of endoscopic images of lower esophagus for abnormal region detection and identification of cancerous growth. Several color image segmentation techniques have been developed for automatic detection of cancerous regions in endoscopic images, which assists the physician for faster, proper diagnosis and treatment of the disease. These segmentation methods are evaluated for comparing their performances in different color spaces, namely, RGB, HSI, YCbCr, HSV, and CIE Lab. The segmented images are expected to assist the medical expert in drawing the biopsy samples precisely from the detected pathological legions. Further, various methods have been proposed for segmentation and classification of squamous cell carcinoma (SCC) from color microscopic images of esophagus tissue during pathological investigation. The efficacy of these methods has been demonstrated experimentally with endoscopic and microscopic image set and compared with manual segmentation done by medical experts. It is envisaged that the research in this direction eventually leads to the design and production of efficient intelligent computer vision systems for assisting the medical experts in their task of speedy accurate diagnosis of diseases and prescription of appropriate treatment of the patients.
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