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dc.contributor.authorSavitha, G.
dc.contributor.authorJidesh, P.
dc.date.accessioned2020-03-31T06:51:11Z-
dc.date.available2020-03-31T06:51:11Z-
dc.date.issued2019
dc.identifier.citationBiomedical Signal Processing and Control, 2019, Vol.53, , pp.-en_US
dc.identifier.uri10.1016/j.bspc.2019.101586
dc.identifier.urihttp://idr.nitk.ac.in/jspui/handle/123456789/9599-
dc.description.abstractA fully-automated computer-aided detection (CAD) system is being proposed in this paper for identification and classification of subsolid lung nodules present in Computed Tomography(CT) scans. The system consists of two stages. The first stage aims at detecting locations of the nodules, while the second one classifies the same into the solid and subsolid category. The system performs segmentation of the region of interest (ROI) and extraction of relevant features from the segmented ROI. Graylevel covariance matrix (GLCM) is being used to extract the Feature vectors. Principle component analysis (PCA) algorithm is used to select significant features in the feature space formed by the vectors. The nodule localization is performed using support vector machine, fuzzy C-means, and random forest classification algorithms. The identified nodules are further grouped into solid and subsolid nodules by extracting histogram of gradient (HoG) features adopting K-means and support vector machine (SVM) classifiers. A large number of annotated images from the widely available benchmark database is tested to validate the results. Efficiency and reliability of the system are evaluated visually and numerically using the relevant quantitative measures. The developed CAD system is found to identify subsolid nodules with a high percentage of accuracy. 2019 Elsevier Ltden_US
dc.titleA fully-automated system for identification and classification of subsolid nodules in lung computed tomographic scansen_US
dc.typeArticleen_US
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