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Automatic Detection of Abnormalities Associated with Abdomen and Liver Images: A Survey on Segmentation Methods

by G.G. Rajput, Anand M. Chavan
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 140 - Number 4
Year of Publication: 2016
Authors: G.G. Rajput, Anand M. Chavan

G.G. Rajput, Anand M. Chavan . Automatic Detection of Abnormalities Associated with Abdomen and Liver Images: A Survey on Segmentation Methods. International Journal of Computer Applications. 140, 4 ( April 2016), 1-9. DOI=10.5120/ijca2016909271

@article{ 10.5120/ijca2016909271,
author = { G.G. Rajput, Anand M. Chavan },
title = { Automatic Detection of Abnormalities Associated with Abdomen and Liver Images: A Survey on Segmentation Methods },
journal = { International Journal of Computer Applications },
issue_date = { April 2016 },
volume = { 140 },
number = { 4 },
month = { April },
year = { 2016 },
issn = { 0975-8887 },
pages = { 1-9 },
numpages = {9},
url = { },
doi = { 10.5120/ijca2016909271 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
%0 Journal Article
%1 2024-02-06T23:41:20.751797+05:30
%A G.G. Rajput
%A Anand M. Chavan
%T Automatic Detection of Abnormalities Associated with Abdomen and Liver Images: A Survey on Segmentation Methods
%J International Journal of Computer Applications
%@ 0975-8887
%V 140
%N 4
%P 1-9
%D 2016
%I Foundation of Computer Science (FCS), NY, USA

Image segmentation plays an important role in medical imaging by automating detection of false structures and other regions of interest. An image segmentation method partitions an image into multiple segments, representing an image into more meaningful, simpler and easier to analyze. Several general-purpose algorithm and techniques have been developed for image segmentation. This paper explains different segmentation techniques used in medical image analysis addressing the segmentation of abdominal and liver images as case study. Experiments are performed on abdominal and liver CT scan images and the outcomes of these segmentation techniques are discussed. Performance of the methods is presented on the basis of parameters namely, pixel values, mean and standard deviation.

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Index Terms

Computer Science
Information Sciences


Segmentation thresholding clustering artificial neural network edge detection region of interest