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USING GREY-SCALE HIT-OR-MISS TRANSFORM FOR DETECTION OF ISOLATED FOREGROUND PIXELS IN CEREBRAL RMN DATASETS
Abstract
MRI images contain a lot of subtle information related to various lesions which are difficult to be picked up by radiologists. Computer aided diagnosis CAD is a valuable tool to improve the ability of an average radiologist to diagnose the subtle lesions. This study uses the isolated foreground pixels in MRI images as a feature able to discern a stroke patient by a healthy one. Brain MRI image was divided into eight equal sectors. The isolated foreground pixels (i.e. pixels satisfying a neighborhood configuration that corresponds to an isolated foreground pixel) were extracted using hit-or-miss and skeleton transformations. We have tested the proposed algorithms using two cerebral image datasets (healthy and acute stroke patients). The mean ? SD values of isolated foreground pixels for stroke patients systematically exceed the corresponding values for healthy patients. The higher numbers of isolated foreground pixel determined using hit-or-miss transform indicate this method as a promising approach for a simple and quick evaluation of stroke.
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