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      Automated medical image segmentation techniques

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          Abstract

          Accurate segmentation of medical images is a key step in contouring during radiotherapy planning. Computed topography (CT) and Magnetic resonance (MR) imaging are the most widely used radiographic techniques in diagnosis, clinical studies and treatment planning. This review provides details of automated segmentation methods, specifically discussed in the context of CT and MR images. The motive is to discuss the problems encountered in segmentation of CT and MR images, and the relative merits and limitations of methods currently available for segmentation of medical images.

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          Most cited references71

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          Statistical pattern recognition: a review

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            A review on image segmentation techniques

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              A modified fuzzy C-means algorithm for bias field estimation and segmentation of MRI data.

              In this paper, we present a novel algorithm for fuzzy segmentation of magnetic resonance imaging (MRI) data and estimation of intensity inhomogeneities using fuzzy logic. MRI intensity inhomogeneities can be attributed to imperfections in the radio-frequency coils or to problems associated with the acquisition sequences. The result is a slowly varying shading artifact over the image that can produce errors with conventional intensity-based classification. Our algorithm is formulated by modifying the objective function of the standard fuzzy c-means (FCM) algorithm to compensate for such inhomogeneities and to allow the labeling of a pixel (voxel) to be influenced by the labels in its immediate neighborhood. The neighborhood effect acts as a regularizer and biases the solution toward piecewise-homogeneous labelings. Such a regularization is useful in segmenting scans corrupted by salt and pepper noise. Experimental results on both synthetic images and MR data are given to demonstrate the effectiveness and efficiency of the proposed algorithm.
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                Author and article information

                Journal
                J Med Phys
                JMP
                Journal of Medical Physics / Association of Medical Physicists of India
                Medknow Publications (India )
                0971-6203
                1998-3913
                Jan-Mar 2010
                : 35
                : 1
                : 3-14
                Affiliations
                School of Biomedical Engineering, Institute of Technology, Institute of Medical Sciences, Banaras Hindu University, Varanasi-221 005, UP, India
                [1 ]Department of Radiotherapy and Radiation Medicine, Institute of Medical Sciences, Banaras Hindu University, Varanasi-221 005, UP, India
                Author notes
                Address for correspondence: Dr. Neeraj Sharma School of Biomedical Engineering, Institute of Technology, Banaras Hindu University, Varanasi-221 005, UP, India E-mail: er_neeraj29@ 123456indiatimes.com
                Article
                JMP-35-3
                10.4103/0971-6203.58777
                2825001
                20177565
                a7e983f3-fe70-46b0-bd00-c44cf466ff4e
                © Journal of Medical Physics

                This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 25 April 2009
                : 15 July 2009
                : 24 August 2009
                Categories
                Review Article

                Medical physics
                computed tomography,medical images artifacts,segmentation,artificial intelligence techniques,magnetic resonance imaging

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