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Diagnosis of Esophagitis Based on Face Recognition Techniques

Face recognition technology has evolved over years with the Principal Component Analysis (PCA) method being the benchmark for recognition efficiency. The face recognition techniques take care of variation of illumination, pose and other features of the face in the image. We envisage an application o...

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Detalles Bibliográficos
Autores principales: Saraf, Santosh S, Udupi, Gururaj R, Hajare, Santosh D
Formato: Texto
Lenguaje:English
Publicado: Bentham Open 2010
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2916207/
https://www.ncbi.nlm.nih.gov/pubmed/20694157
http://dx.doi.org/10.2174/1874431101004020058
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author Saraf, Santosh S
Udupi, Gururaj R
Hajare, Santosh D
author_facet Saraf, Santosh S
Udupi, Gururaj R
Hajare, Santosh D
author_sort Saraf, Santosh S
collection PubMed
description Face recognition technology has evolved over years with the Principal Component Analysis (PCA) method being the benchmark for recognition efficiency. The face recognition techniques take care of variation of illumination, pose and other features of the face in the image. We envisage an application of these face recognition techniques for classification of medical images. The motivating factor being, given a condition of an organ it is represented by some typical features. In this paper we report the use of the face recognition techniques to classify the type of Esophagitis, a condition of inflammation of the esophagus. The image of the esophagus is captured in the process of endoscopy. We test PCA, Fisher Face method and Independent Component Analysis techniques to classify the images of the esophagus. Esophagitis is classified into four categories. The results of classification for each method are reported and the results are compared.
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spelling pubmed-29162072010-08-05 Diagnosis of Esophagitis Based on Face Recognition Techniques Saraf, Santosh S Udupi, Gururaj R Hajare, Santosh D Open Med Inform J Article Face recognition technology has evolved over years with the Principal Component Analysis (PCA) method being the benchmark for recognition efficiency. The face recognition techniques take care of variation of illumination, pose and other features of the face in the image. We envisage an application of these face recognition techniques for classification of medical images. The motivating factor being, given a condition of an organ it is represented by some typical features. In this paper we report the use of the face recognition techniques to classify the type of Esophagitis, a condition of inflammation of the esophagus. The image of the esophagus is captured in the process of endoscopy. We test PCA, Fisher Face method and Independent Component Analysis techniques to classify the images of the esophagus. Esophagitis is classified into four categories. The results of classification for each method are reported and the results are compared. Bentham Open 2010-05-28 /pmc/articles/PMC2916207/ /pubmed/20694157 http://dx.doi.org/10.2174/1874431101004020058 Text en © Saraf et al.; Licensee Bentham Open. http://creativecommons.org/licenses/by-nc/3.0/ This is an open access article licensed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/) which permits unrestricted, non-commercial use, distribution and reproduction in any medium, provided the work is properly cited.
spellingShingle Article
Saraf, Santosh S
Udupi, Gururaj R
Hajare, Santosh D
Diagnosis of Esophagitis Based on Face Recognition Techniques
title Diagnosis of Esophagitis Based on Face Recognition Techniques
title_full Diagnosis of Esophagitis Based on Face Recognition Techniques
title_fullStr Diagnosis of Esophagitis Based on Face Recognition Techniques
title_full_unstemmed Diagnosis of Esophagitis Based on Face Recognition Techniques
title_short Diagnosis of Esophagitis Based on Face Recognition Techniques
title_sort diagnosis of esophagitis based on face recognition techniques
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2916207/
https://www.ncbi.nlm.nih.gov/pubmed/20694157
http://dx.doi.org/10.2174/1874431101004020058
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