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Studies of Suspicious Lesions through Local Texture Analysis in Spine Radiographs

by Richa Sharma, T. R. Gopalakrishnan Nair
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 146 - Number 2
Year of Publication: 2016
Authors: Richa Sharma, T. R. Gopalakrishnan Nair
10.5120/ijca2016910698

Richa Sharma, T. R. Gopalakrishnan Nair . Studies of Suspicious Lesions through Local Texture Analysis in Spine Radiographs. International Journal of Computer Applications. 146, 2 ( Jul 2016), 35-40. DOI=10.5120/ijca2016910698

@article{ 10.5120/ijca2016910698,
author = { Richa Sharma, T. R. Gopalakrishnan Nair },
title = { Studies of Suspicious Lesions through Local Texture Analysis in Spine Radiographs },
journal = { International Journal of Computer Applications },
issue_date = { Jul 2016 },
volume = { 146 },
number = { 2 },
month = { Jul },
year = { 2016 },
issn = { 0975-8887 },
pages = { 35-40 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume146/number2/25374-2016910698/ },
doi = { 10.5120/ijca2016910698 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:49:15.350411+05:30
%A Richa Sharma
%A T. R. Gopalakrishnan Nair
%T Studies of Suspicious Lesions through Local Texture Analysis in Spine Radiographs
%J International Journal of Computer Applications
%@ 0975-8887
%V 146
%N 2
%P 35-40
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This paper presents a method to assess and classify the textural abnormalities created by the cellular changes made available through digital radiographs. In this process, digital X-ray images of the spine were subjected to investigations. A study has been made to choose the optimum set of features selectively to train the classifier. In most common applications, the feature vector is extracted from the image data using pixel intensity and magnitude of the frequency displayed as an image. In this case, the introduction of the phase angle of the frequency involved in generating the hue of the suspicious lesions considerably enhanced the success rate of the detection (approximately by 25%). Our experimental study based on 32 subjects indicates that the proposed system is successfully classifying the abnormal regions with 94.37% mean Correct Classification Ratio (CCR). Here, CCR is defined as correctly classified samples versus all the samples.

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Index Terms

Computer Science
Information Sciences

Keywords

Classification Segmentation Computer-aided detection (CAD) Spinal Tuberculosis (TB).

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