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Classification of Normal, Benign and Malignant Tissues using Fuzzy Texton and Support Vector Machine in Mammographic Images

by Venkata Ragha Deepthi Loka, Sudhakar Putheti
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 82 - Number 15
Year of Publication: 2013
Authors: Venkata Ragha Deepthi Loka, Sudhakar Putheti
10.5120/14243-2450

Venkata Ragha Deepthi Loka, Sudhakar Putheti . Classification of Normal, Benign and Malignant Tissues using Fuzzy Texton and Support Vector Machine in Mammographic Images. International Journal of Computer Applications. 82, 15 ( November 2013), 36-39. DOI=10.5120/14243-2450

@article{ 10.5120/14243-2450,
author = { Venkata Ragha Deepthi Loka, Sudhakar Putheti },
title = { Classification of Normal, Benign and Malignant Tissues using Fuzzy Texton and Support Vector Machine in Mammographic Images },
journal = { International Journal of Computer Applications },
issue_date = { November 2013 },
volume = { 82 },
number = { 15 },
month = { November },
year = { 2013 },
issn = { 0975-8887 },
pages = { 36-39 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume82/number15/14243-2450/ },
doi = { 10.5120/14243-2450 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:57:52.147753+05:30
%A Venkata Ragha Deepthi Loka
%A Sudhakar Putheti
%T Classification of Normal, Benign and Malignant Tissues using Fuzzy Texton and Support Vector Machine in Mammographic Images
%J International Journal of Computer Applications
%@ 0975-8887
%V 82
%N 15
%P 36-39
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Content Based Image Retrieval systems are helpful to the radiologists in diagnosis of breast cancer. This paper presents a method for retrieving breast tissue as normal, benign or malignant in mammograms by using FuzzyTextons. In feature extraction first fuzzy texton images of mammograms are calculated. During the detection of fuzzy texton, fuzzy based quantization is performed to get more accurate textons. Then feature vectors are extracted for fuzzy textons and for efficient classification and retrieval Support Vector Machine is used. The proposed method was tested for a mammogram set from MIAS database.

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Index Terms

Computer Science
Information Sciences

Keywords

Breast cancer FuzzyTextons Mammogram SVM

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