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 the ori...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2015-01-01
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| Series: | International Journal of Biomedical Imaging |
| Online Access: | http://dx.doi.org/10.1155/2015/230830 |
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| _version_ | 1832545802362290176 |
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| author | Eman Magdy Nourhan Zayed Mahmoud Fakhr |
| author_facet | Eman Magdy Nourhan Zayed Mahmoud Fakhr |
| author_sort | Eman Magdy |
| collection | DOAJ |
| 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 | Article |
| id | doaj-art-a56cbb5718a2489e8ba519adb537bb43 |
| institution | Kabale University |
| issn | 1687-4188 1687-4196 |
| language | English |
| publishDate | 2015-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Biomedical Imaging |
| spelling | doaj-art-a56cbb5718a2489e8ba519adb537bb432025-02-03T07:24:41ZengWileyInternational Journal of Biomedical Imaging1687-41881687-41962015-01-01201510.1155/2015/230830230830Automatic Classification of Normal and Cancer Lung CT Images Using Multiscale AM-FM FeaturesEman Magdy0Nourhan Zayed1Mahmoud Fakhr2Computer and Systems Department, Electronic Research Institute, Giza 12611, EgyptComputer and Systems Department, Electronic Research Institute, Giza 12611, EgyptComputer and Systems Department, Electronic Research Institute, Giza 12611, EgyptComputer-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.http://dx.doi.org/10.1155/2015/230830 |
| spellingShingle | Eman Magdy Nourhan Zayed Mahmoud Fakhr Automatic Classification of Normal and Cancer Lung CT Images Using Multiscale AM-FM Features International Journal of Biomedical Imaging |
| 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 |
| url | http://dx.doi.org/10.1155/2015/230830 |
| work_keys_str_mv | AT emanmagdy automaticclassificationofnormalandcancerlungctimagesusingmultiscaleamfmfeatures AT nourhanzayed automaticclassificationofnormalandcancerlungctimagesusingmultiscaleamfmfeatures AT mahmoudfakhr automaticclassificationofnormalandcancerlungctimagesusingmultiscaleamfmfeatures |