title: A Combination of Shape and Texture Features for Classification of Pulmonary Nodules in Lung CT Images.
abstract: Classification of malignant and benign pulmonary nodules is important for further treatment plan. The present work focuses on the classification of benign and malignant pulmonary nodules using support vector machine. The pulmonary nodules are segmented using a semi-automated technique, which requires only a seed point from the end user. Several shape-based, margin-based, and texture-based features are computed to represent the pulmonary nodules. A set of relevant features is determined for the efficient representation of nodules in the feature space. The proposed classification scheme is validated on a data set of 891 nodules of Lung Image Database Consortium and Image Database Resource Initiative public database. The proposed classification scheme is evaluated for three configurations such as configuration 1 (composite rank of malignancy "1" and "2" as benign and "4" and "5" as malignant), configuration 2 (composite rank of malignancy "1","2", and "3" as benign and "4" and "5" as malignant), and configuration 3 (composite rank of malignancy "1" and "2" as benign and "3","4" and "5" as malignant). The performance of the classification is evaluated in terms of area (A z) under the receiver operating characteristic curve. The A z achieved by the proposed method for configuration-1, configuration-2, and configuration-3 are 0.9505, 0.8822, and 0.8488, respectively. The proposed method outperforms the most recent technique, which depends on the manual segmentation of pulmonary nodules by a trained radiologist.
authors.affiliation: Electronics and Electrical Communication Engineering, Indian Institute of Technology, Kharagpur, 721302, India.
authors.forename: Ashis Kumar
authors.initials: AK
authors.lastname: Dhara
authors.affiliation: Electronics and Electrical Communication Engineering, Indian Institute of Technology, Kharagpur, 721302, India. smukho@gmail.com.
authors.forename: Sudipta
authors.initials: S
authors.lastname: Mukhopadhyay
authors.affiliation: Electronics and Communication Engineering, Birla Institute of Technology Mesra, Ranchi, 835215, India.
authors.forename: Anirvan
authors.initials: A
authors.lastname: Dutta
authors.affiliation: Department of Radio Diagnosis & Imaging, Post Graduate Institute of Medical Education and Research, Chandigarh, 160 012, India.
authors.forename: Mandeep
authors.initials: M
authors.lastname: Garg
authors.affiliation: Department of Radio Diagnosis & Imaging, Post Graduate Institute of Medical Education and Research, Chandigarh, 160 012, India.
authors.forename: Niranjan
authors.initials: N
authors.lastname: Khandelwal
mesh_terms: D000465:Algorithms; D006801:Humans; D008175:Lung Neoplasms / Q000145:classification / Q000000981:diagnostic imaging*; D011857:Radiographic Image Interpretation, Computer-Assisted; D003074:Solitary Pulmonary Nodule / Q000145:classification / Q000000981:diagnostic imaging*; D014057:Tomography, X-Ray Computed
keywords: CT images; Classification of benign and malignant nodules; Feature extraction; Features selection; Lung cancer; Pulmonary nodules; Segmentation of nodules