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Number 1

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

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  • Ajala F.a and Emuoyibofarhe J.o 2012. Enhanced Geometric Active Contour Segmentation Model (ENGAC) For Medical Image Segmentation. International Journal of Applied Information Systems. 3, 1 (July 2012), 1-8. DOI=http://dx.doi.org/10.5120/ijais450422 
  • @article{10.5120/ijais2017451568,
author = {Ajala F.a and Emuoyibofarhe J.o},
title = {Enhanced Geometric Active Contour Segmentation Model (ENGAC) For Medical Image Segmentation},
journal = {International Journal of Applied Information Systems},
issue_date = {July 2012},
volume = {3},
number = {},
month = {July},
year = {2012},
issn = {},
pages = {1-8},
numpages = {},
url = {/archives/volume3/number1/193-0422},
doi = { http:/ijais12-450422},
publisher = { xA9 2010 by IJAIS Journal},
address = {}
}

  • %1 450422
%A Ajala F. A
%A Emuoyibofarhe J. O
%T Enhanced Geometric Active Contour Segmentation Model (ENGAC) For Medical Image Segmentation
%J International Journal of Applied Information Systems
%@ 
%V 3
%N 
%P 1-8
%D 2012
%I  xA9 2010 by IJAIS Journal

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.

References

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Keywords

Geometric Active Contour, Mri, Ct, Segmentation

Index Terms

Computer Science
Information Sciences