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Singular Value Decomposition Based Features for Automatic Tumor Detection in Wireless Capsule Endoscopy Images
Wireless capsule endoscopy (WCE) is a new noninvasive instrument which allows direct observation of the gastrointestinal tract to diagnose its relative diseases. Because of the large number of images obtained from the capsule endoscopy per patient, doctors need too much time to investigate all of th...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi Publishing Corporation
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4958451/ https://www.ncbi.nlm.nih.gov/pubmed/27478364 http://dx.doi.org/10.1155/2016/3678913 |
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author | Faghih Dinevari, Vahid Karimian Khosroshahi, Ghader Zolfy Lighvan, Mina |
author_facet | Faghih Dinevari, Vahid Karimian Khosroshahi, Ghader Zolfy Lighvan, Mina |
author_sort | Faghih Dinevari, Vahid |
collection | PubMed |
description | Wireless capsule endoscopy (WCE) is a new noninvasive instrument which allows direct observation of the gastrointestinal tract to diagnose its relative diseases. Because of the large number of images obtained from the capsule endoscopy per patient, doctors need too much time to investigate all of them. So, it would be worthwhile to design a system for detecting diseases automatically. In this paper, a new method is presented for automatic detection of tumors in the WCE images. This method will utilize the advantages of the discrete wavelet transform (DWT) and singular value decomposition (SVD) algorithms to extract features from different color channels of the WCE images. Therefore, the extracted features are invariant to rotation and can describe multiresolution characteristics of the WCE images. In order to classify the WCE images, the support vector machine (SVM) method is applied to a data set which includes 400 normal and 400 tumor WCE images. The experimental results show proper performance of the proposed algorithm for detection and isolation of the tumor images which, in the best way, shows 94%, 93%, and 93.5% of sensitivity, specificity, and accuracy in the RGB color space, respectively. |
format | Online Article Text |
id | pubmed-4958451 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | Hindawi Publishing Corporation |
record_format | MEDLINE/PubMed |
spelling | pubmed-49584512016-07-31 Singular Value Decomposition Based Features for Automatic Tumor Detection in Wireless Capsule Endoscopy Images Faghih Dinevari, Vahid Karimian Khosroshahi, Ghader Zolfy Lighvan, Mina Appl Bionics Biomech Research Article Wireless capsule endoscopy (WCE) is a new noninvasive instrument which allows direct observation of the gastrointestinal tract to diagnose its relative diseases. Because of the large number of images obtained from the capsule endoscopy per patient, doctors need too much time to investigate all of them. So, it would be worthwhile to design a system for detecting diseases automatically. In this paper, a new method is presented for automatic detection of tumors in the WCE images. This method will utilize the advantages of the discrete wavelet transform (DWT) and singular value decomposition (SVD) algorithms to extract features from different color channels of the WCE images. Therefore, the extracted features are invariant to rotation and can describe multiresolution characteristics of the WCE images. In order to classify the WCE images, the support vector machine (SVM) method is applied to a data set which includes 400 normal and 400 tumor WCE images. The experimental results show proper performance of the proposed algorithm for detection and isolation of the tumor images which, in the best way, shows 94%, 93%, and 93.5% of sensitivity, specificity, and accuracy in the RGB color space, respectively. Hindawi Publishing Corporation 2016 2016-07-10 /pmc/articles/PMC4958451/ /pubmed/27478364 http://dx.doi.org/10.1155/2016/3678913 Text en Copyright © 2016 Vahid Faghih Dinevari et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Faghih Dinevari, Vahid Karimian Khosroshahi, Ghader Zolfy Lighvan, Mina Singular Value Decomposition Based Features for Automatic Tumor Detection in Wireless Capsule Endoscopy Images |
title | Singular Value Decomposition Based Features for Automatic Tumor Detection in Wireless Capsule Endoscopy Images |
title_full | Singular Value Decomposition Based Features for Automatic Tumor Detection in Wireless Capsule Endoscopy Images |
title_fullStr | Singular Value Decomposition Based Features for Automatic Tumor Detection in Wireless Capsule Endoscopy Images |
title_full_unstemmed | Singular Value Decomposition Based Features for Automatic Tumor Detection in Wireless Capsule Endoscopy Images |
title_short | Singular Value Decomposition Based Features for Automatic Tumor Detection in Wireless Capsule Endoscopy Images |
title_sort | singular value decomposition based features for automatic tumor detection in wireless capsule endoscopy images |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4958451/ https://www.ncbi.nlm.nih.gov/pubmed/27478364 http://dx.doi.org/10.1155/2016/3678913 |
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