An Enhanced Texture-Based Feature Extraction Approach for Classification of Biomedical Images of CT-Scan of Lungs

An Enhanced Texture-Based Feature Extraction Approach for Classification of Biomedical Images of CT-Scan of Lungs

Content Based Image Retrieval (CBIR) techniques based on texture have gained a lot of popularity in recent times. In the proposed work, a feature vector is obtained by concatenation of features extracted from local mesh peak valley edge pattern (LMePVEP) technique; a dynamic threshold based local me...

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Main Authors: Varun Srivastava, Shilpa Gupta, Gopal Chaudhary, Arun Balodi, Manju Khari, Vicente García-Díaz
Format: Article
Language:English
Published: Universidad Internacional de La Rioja (UNIR) 2021-09-01
Series:International Journal of Interactive Multimedia and Artificial Intelligence
Subjects:
image classification
local mesh peak valley edge patterns
patterns
information retrieval
Online Access:https://www.ijimai.org/journal/bibcite/reference/2830
id doaj-c8f368cc4bac40ba8add68b914c24495
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spelling doaj-c8f368cc4bac40ba8add68b914c244952021-09-11T22:07:29ZengUniversidad Internacional de La Rioja (UNIR)International Journal of Interactive Multimedia and Artificial Intelligence1989-16602021-09-0167182510.9781/ijimai.2020.11.003ijimai.2020.11.003An Enhanced Texture-Based Feature Extraction Approach for Classification of Biomedical Images of CT-Scan of LungsVarun SrivastavaShilpa GuptaGopal ChaudharyArun BalodiManju KhariVicente García-DíazContent Based Image Retrieval (CBIR) techniques based on texture have gained a lot of popularity in recent times. In the proposed work, a feature vector is obtained by concatenation of features extracted from local mesh peak valley edge pattern (LMePVEP) technique; a dynamic threshold based local mesh ternary pattern technique and texture of the image in five different directions. The concatenated feature vector is then used to classify images of two datasets viz. Emphysema dataset and Early Lung Cancer Action Program (ELCAP) lung database. The proposed framework has improved the accuracy by 12.56%, 9.71% and 7.01% in average for data set 1 and 9.37%, 8.99% and 7.63% in average for dataset 2 over three popular algorithms used for image retrieval.https://www.ijimai.org/journal/bibcite/reference/2830image classificationlocal mesh peak valley edge patternspatternsinformation retrieval
collection DOAJ
language English
format Article
sources DOAJ
author Varun Srivastava
Shilpa Gupta
Gopal Chaudhary
Arun Balodi
Manju Khari
Vicente García-Díaz
spellingShingle Varun Srivastava
Shilpa Gupta
Gopal Chaudhary
Arun Balodi
Manju Khari
Vicente García-Díaz
An Enhanced Texture-Based Feature Extraction Approach for Classification of Biomedical Images of CT-Scan of Lungs
International Journal of Interactive Multimedia and Artificial Intelligence
image classification
local mesh peak valley edge patterns
patterns
information retrieval
author_facet Varun Srivastava
Shilpa Gupta
Gopal Chaudhary
Arun Balodi
Manju Khari
Vicente García-Díaz
author_sort Varun Srivastava
title An Enhanced Texture-Based Feature Extraction Approach for Classification of Biomedical Images of CT-Scan of Lungs
title_short An Enhanced Texture-Based Feature Extraction Approach for Classification of Biomedical Images of CT-Scan of Lungs
title_full An Enhanced Texture-Based Feature Extraction Approach for Classification of Biomedical Images of CT-Scan of Lungs
title_fullStr An Enhanced Texture-Based Feature Extraction Approach for Classification of Biomedical Images of CT-Scan of Lungs
title_full_unstemmed An Enhanced Texture-Based Feature Extraction Approach for Classification of Biomedical Images of CT-Scan of Lungs
title_sort enhanced texture-based feature extraction approach for classification of biomedical images of ct-scan of lungs
publisher Universidad Internacional de La Rioja (UNIR)
series International Journal of Interactive Multimedia and Artificial Intelligence
issn 1989-1660
publishDate 2021-09-01
description Content Based Image Retrieval (CBIR) techniques based on texture have gained a lot of popularity in recent times. In the proposed work, a feature vector is obtained by concatenation of features extracted from local mesh peak valley edge pattern (LMePVEP) technique; a dynamic threshold based local mesh ternary pattern technique and texture of the image in five different directions. The concatenated feature vector is then used to classify images of two datasets viz. Emphysema dataset and Early Lung Cancer Action Program (ELCAP) lung database. The proposed framework has improved the accuracy by 12.56%, 9.71% and 7.01% in average for data set 1 and 9.37%, 8.99% and 7.63% in average for dataset 2 over three popular algorithms used for image retrieval.
topic image classification
local mesh peak valley edge patterns
patterns
information retrieval
url https://www.ijimai.org/journal/bibcite/reference/2830
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