Automatic Classification of Normal and Cancer Lung CT Images Using Multiscale AM-FM Features. (15th September 2015)

• Record Type: Journal Article
• Authors: Magdy, Eman; Zayed, Nourhan; Fakhr, Mahmoud
• Other Names: Zhuang Tiange Academic Editor.
• Is Part Of: International journal of biomedical imaging. Volume 2015(2015)
• Publication Date: 2015-09-15
• Subjects: Diagnostic imaging -- Periodicals; Imaging systems in medicine -- Periodicals; Imagerie pour le diagnostic; Imagerie médicale; Diagnostic imaging; Imaging systems in medicine; Diagnostic Imaging -- Periodicals; Electronic journals; Periodicals; 616.0754
• DOI: 10.1155/2015/230830
• Languages: English
• ISSNs: 1687-4188

Abstract

Abstract : 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 (K NN), 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.