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Automatic Classification of Normal and Cancer Lung CT Images Using Multiscale AM-FM Features
Computer-aided diagnostic (CAD) systems provide fast and reliable diagnosis for medical images. In this paper, CAD system is proposed to analyze and automatically segment the lungs and classify each lung into normal or cancer. Using 70 different patients' lung CT dataset, Wiener filtering on th...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi Publishing Corporation
2015
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4587437/ https://www.ncbi.nlm.nih.gov/pubmed/26451137 http://dx.doi.org/10.1155/2015/230830 |
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author | Magdy, Eman Zayed, Nourhan Fakhr, Mahmoud |
author_facet | Magdy, Eman Zayed, Nourhan Fakhr, Mahmoud |
author_sort | Magdy, Eman |
collection | PubMed |
description | Computer-aided diagnostic (CAD) systems provide fast and reliable diagnosis for medical images. In this paper, CAD system is proposed to analyze and automatically segment the lungs and classify each lung into normal or cancer. Using 70 different patients' lung CT dataset, Wiener filtering on the original CT images is applied firstly as a preprocessing step. Secondly, we combine histogram analysis with thresholding and morphological operations to segment the lung regions and extract each lung separately. Amplitude-Modulation Frequency-Modulation (AM-FM) method thirdly, has been used to extract features for ROIs. Then, the significant AM-FM features have been selected using Partial Least Squares Regression (PLSR) for classification step. Finally, K-nearest neighbour (KNN), support vector machine (SVM), naïve Bayes, and linear classifiers have been used with the selected AM-FM features. The performance of each classifier in terms of accuracy, sensitivity, and specificity is evaluated. The results indicate that our proposed CAD system succeeded to differentiate between normal and cancer lungs and achieved 95% accuracy in case of the linear classifier. |
format | Online Article Text |
id | pubmed-4587437 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | Hindawi Publishing Corporation |
record_format | MEDLINE/PubMed |
spelling | pubmed-45874372015-10-08 Automatic Classification of Normal and Cancer Lung CT Images Using Multiscale AM-FM Features Magdy, Eman Zayed, Nourhan Fakhr, Mahmoud Int J Biomed Imaging Research Article Computer-aided diagnostic (CAD) systems provide fast and reliable diagnosis for medical images. In this paper, CAD system is proposed to analyze and automatically segment the lungs and classify each lung into normal or cancer. Using 70 different patients' lung CT dataset, Wiener filtering on the original CT images is applied firstly as a preprocessing step. Secondly, we combine histogram analysis with thresholding and morphological operations to segment the lung regions and extract each lung separately. Amplitude-Modulation Frequency-Modulation (AM-FM) method thirdly, has been used to extract features for ROIs. Then, the significant AM-FM features have been selected using Partial Least Squares Regression (PLSR) for classification step. Finally, K-nearest neighbour (KNN), support vector machine (SVM), naïve Bayes, and linear classifiers have been used with the selected AM-FM features. The performance of each classifier in terms of accuracy, sensitivity, and specificity is evaluated. The results indicate that our proposed CAD system succeeded to differentiate between normal and cancer lungs and achieved 95% accuracy in case of the linear classifier. Hindawi Publishing Corporation 2015 2015-09-15 /pmc/articles/PMC4587437/ /pubmed/26451137 http://dx.doi.org/10.1155/2015/230830 Text en Copyright © 2015 Eman Magdy et al. https://creativecommons.org/licenses/by/3.0/ 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 Magdy, Eman Zayed, Nourhan Fakhr, Mahmoud Automatic Classification of Normal and Cancer Lung CT Images Using Multiscale AM-FM Features |
title | Automatic Classification of Normal and Cancer Lung CT Images Using Multiscale AM-FM Features |
title_full | Automatic Classification of Normal and Cancer Lung CT Images Using Multiscale AM-FM Features |
title_fullStr | Automatic Classification of Normal and Cancer Lung CT Images Using Multiscale AM-FM Features |
title_full_unstemmed | Automatic Classification of Normal and Cancer Lung CT Images Using Multiscale AM-FM Features |
title_short | Automatic Classification of Normal and Cancer Lung CT Images Using Multiscale AM-FM Features |
title_sort | automatic classification of normal and cancer lung ct images using multiscale am-fm features |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4587437/ https://www.ncbi.nlm.nih.gov/pubmed/26451137 http://dx.doi.org/10.1155/2015/230830 |
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