Brain MRI Segmentation using Simple Linear Iterative Clustering Based Superpixel approach on conventional Clustering algorithms
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초록
Various kinds of methods are being introduced recently to help in Image segmentation of medical image which is of immense use and application. Similarly, Brain MRI Image Segmentation has always been a thought-provoking job, which is of immense importance for clinical application and scientific research. Various approach have been proposed for automatic, semiautomatic and manual based segmentation utilizing different datasets available online or local database of a medical institution, in order to obtain more precise and accurately distinguishable segments. Here, we are applying a super pixel generation Simple Linear Iterative Clustering (SLIC) algorithm to make the image more suitable for clustering pixel of same intensity levels of closely related groups. Application of Super pixel along with Kmeans or EM algorithm helps to make clustering more robust and clear. Therefore the resulted outcome is represented as RGB based color-segmented image of White matter, Grey matter, CSF, and background by using mid-Cross-sectional brain of normal human brain image.
Abstract
Various kinds of methods are being introduced recently to help in Image segmentation of medical image which is of immense use and application. Similarly, Brain MRI Image Segmentation has always been a thought-provoking job, which is of immense importance for clinical application and scientific research. Various approach have been proposed for automatic, semiautomatic and manual based segmentation utilizing different datasets available online or local database of a medical institution, in order to obtain more precise and accurately distinguishable segments. Here, we are applying a super pixel generation Simple Linear Iterative Clustering (SLIC) algorithm to make the image more suitable for clustering pixel of same intensity levels of closely related groups. Application of Super pixel along with Kmeans or EM algorithm helps to make clustering more robust and clear. Therefore the resulted outcome is represented as RGB based color-segmented image of White matter, Grey matter, CSF, and background by using mid-Cross-sectional brain of normal human brain image.
참고문헌 (14)
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