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Enhanced Geometric Active Contour Segmentation Model (ENGAC) For Medical Image Segmentation

Ajala F. A, Emuoyibofarhe J. O Published in Signal Processing

International Journal of Applied Information Systems
Year of Publication 2012
© 2010 by IJAIS Journal
http:/ijais12-450422
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  1. Ajala F.a and Emuoyibofarhe J.o. Article: Enhanced Geometric Active Contour Segmentation Model (ENGAC) For Medical Image Segmentation. International Journal of Applied Information Systems 3(1):1-8, July 2012. BibTeX

    @article{key:article,
    	author = "Ajala F.a and Emuoyibofarhe J.o",
    	title = "Article: Enhanced Geometric Active Contour Segmentation Model (ENGAC) For Medical Image Segmentation",
    	journal = "International Journal of Applied Information Systems",
    	year = 2012,
    	volume = 3,
    	number = 1,
    	pages = "1-8",
    	month = "July",
    	note = "Published by Foundation of Computer Science, New York, USA"
    }
    

Abstract

Segmentation is an aspect of computer vision that deals with partitioning of an image into homogeneneous region. Medical image segmentation is an indispensable tool for medical image diagnoses. This work built on Geometric active contour (GAC) segmentation which is one of the outstanding model used in machine learning community to solve the problem of medical image segmentation. However, GAC has problem of deviation from the true outline of the target feature and it generates spurious edge caused by noise that normally stop the evolution of the surface to be extracted.

In this paper, an enhanced Geometric active contour was formulated by hybridizing Kernel Principal Component Analysis(KPCA) with the existing Geometric active contour segmentation model . KPCA was used to analyse shape variability in trans-axial human brain medical images collected from University College Hospital Ibadan. The enhanced model was used to segment selected feature from registered normal and pathological Magnetic Resonance Image (MRI) scan and Computed Tomography (CT) scan medical image. Finally, the caudate nucleus from normal human brain and tumour from pathological human brain was detected and segmented using our enhanced model. The segmentation time, volume area segmented and the haudsorf distance was calculated for the new model.

Reference

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Keywords

Geometric Active Contour, Mri, Ct, Segmentation