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Automatic Detection of Liver in CT images using Optimal Feature based Neural Network

by Ritu Punia, Shailendra Singh
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 76 - Number 15
Year of Publication: 2013
Authors: Ritu Punia, Shailendra Singh
10.5120/13326-0903

Ritu Punia, Shailendra Singh . Automatic Detection of Liver in CT images using Optimal Feature based Neural Network. International Journal of Computer Applications. 76, 15 ( August 2013), 53-60. DOI=10.5120/13326-0903

@article{ 10.5120/13326-0903,
author = { Ritu Punia, Shailendra Singh },
title = { Automatic Detection of Liver in CT images using Optimal Feature based Neural Network },
journal = { International Journal of Computer Applications },
issue_date = { August 2013 },
volume = { 76 },
number = { 15 },
month = { August },
year = { 2013 },
issn = { 0975-8887 },
pages = { 53-60 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume76/number15/13326-0903/ },
doi = { 10.5120/13326-0903 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:46:01.188850+05:30
%A Ritu Punia
%A Shailendra Singh
%T Automatic Detection of Liver in CT images using Optimal Feature based Neural Network
%J International Journal of Computer Applications
%@ 0975-8887
%V 76
%N 15
%P 53-60
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This paper presents a method for automatic detection of liver in CT images using optimal texture features. As image contains noise so firstly, image is pre-processed with median filter. Regions of interests are chosen carefully from both liver and non-liver areas. Texture features are extracted from selected regions of interest using first order statistics and wavelet transform. Neural Network is used for classification of pixels into liver and non-liver areas. Accuracy of classification process depends on number of features extracted which should be chosen carefully. For careful selection of features, optimal features are selected from extracted features using genetic algorithm and used for final classification of image pixels. The method is tested on CT images and results obtained are presented both qualitatively and quantitatively.

References
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Index Terms

Computer Science
Information Sciences

Keywords

Liver Detection Texture Optimal Features Genetic Algorithm Neural Network

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