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USING GREY-SCALE HIT-OR-MISS TRANSFORM FOR DETECTION OF ISOLATED FOREGROUND PIXELS IN CEREBRAL RMN DATASETS

Lenuța Pană

First published: 2019-12-05https://doi.org/10.5593/sgem2019v/6.3/s08.030View metrics

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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Publication details

Title
USING GREY-SCALE HIT-OR-MISS TRANSFORM FOR DETECTION OF ISOLATED FOREGROUND PIXELS IN CEREBRAL RMN DATASETS
Authors
Lenuța Pană
Proceedings
SGEM International Multidisciplinary Scientific GeoConference EXPO Proceedings; 19th SGEM International Multidisciplinary Scientific GeoConference EXPO Proceedings19th, Nano, Bio, Green and Space: Technologies for Sustainable Future
Publisher
STEF92 Technology
Year
2019
Pages
229-236
SWS Citekey
Pana20198229236
ISSN
1314-2704
ISBN
978-619-7408-99-7
Language
en
Publication type
Conference Paper
Keywords
References17
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