A fully-automated system for identification and classification of subsolid nodules in lung computed tomographic scans. (August 2019)

• Record Type: Journal Article
• Authors: G., Savitha; P., Jidesh
• Is Part Of: Biomedical signal processing and control. Volume 53(2019)
• Publication Date: 2019-08
• Subjects: Subsolid nodules -- Image restoration -- Image segmentation -- Nodule detection and classification -- Gray-level covariance matrix -- Histogram of gradients; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2019.101586
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: A subsolid lung nodular image segmentation and detection algorithm have been proposed. The automated system proposed here can classify subsolid nodules with high accuracy. The pre-processing activity makes the diagnosis more robust and less sensitive to noise. The classification accuracy is higher as compared to the state-of-the-art models. Various features are analyzed to design the feature vector for the classifier. Abstract: A 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.