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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...

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Detalles Bibliográficos
Autores principales: Magdy, Eman, Zayed, Nourhan, Fakhr, Mahmoud
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi Publishing Corporation 2015
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.
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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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