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Segmentation is used as the first and the most important phase in therecognition and treatment of a disease in analysing medical images likeMRI images It is challenging due to poor contrast and artefacts thatresult in missing or diffuse organtissue boundaries In this paper wewill discuss different ways to automate the segmentation of medicalimages We except further improvements can be achieved by incorporating as much prior information as possible eg texture shape spatiallocation of organs into a single frameworkIntroductionImage data plays a vital role in medical informatics They help inextracting meaningful information such as volume shape motion oforgans to detect abnormalities Thus segmentation is vital in the analysis of medical images which canbe useful in many applications such as distinguishing the arteriesborders from each other in angiography the size of tumour and itsresponse to treatment interpretation of operation the study of braingrowth and recognition of tumour and so on 1 Therefore it is obviousthat segmentation is often used as the first and most important phase inthe recognition and treatment of a disease in analysing medical images Now for the above mentioned optimization problem Genetic algorithms areone of the most powerful techniques in a large solution space They areused where exhaustive search for solution is expensive in terms ofcomputation timeImage Segmentation TechniquesEdge Detection TechniqueEdge detection is a type of image segmentation techniques whichdetermines the presence of an edge or line in an image and outlines themin an appropriate way 27 The main purpose of edge detection is tosimplify the image data in order to minimize the amount of data to beprocessed 6 Generally an edge is defined as the boundary pixels thatconnect two separate regions with changing image amplitude attributessuch as different constant luminance and tristimulus values in an image271828 The detection operation begins with the examination of thelocal discontinuity at each pixel element in an image Amplitudeorientation and location of a particular subarea in the image that isof interest are essentially important characteristics
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