eng Oriental Scientific Publishing Company Biomedical and Pharmacology Journal 0974-6242 2017-12-21 10 4 1949 1961 10.13005/bpj/1315 17589 article Satya Prakash Sahu 1 Priyanka Agrawal 1 Narendra D Londhe 2 ShrishVerma 3 Departmentof Information Technology, National Institute of Technology, Raipur, India. Departmentof Electrical Engineering, National Institute of Technology, Raipur, India. Departmentof Electronics and Telecommunication Engineering, National Institute of Technology, Raipur, India. For computer-aided-diagnosis (CAD) System, the lung segmentation phase is having most significant role in the detection of lung cancer at initial stages. It is needed as preprocessing step for obtaining the accurate Region of Interest (ROI) area. Efficiency of CAD system is mainly depending on how the lungs are precisely segmented. The effective lung segmentation overcomes the various challenges offered in CAD system to deal with the cases of juxtapleural nodules. This paper emphasizes the proposed method for lung segmentation in CT images using clustering approach of fuzzy-c-means with automatic thresholding and morphological operations. The experimental database contains 20 patients’ series (approximately 3600 images) from publically available LIDC-IDRI dataset which includes 10 general cases and 10 cases having juxtapleural nodules in pulmonary region. Reference standard contours are prepared by the expert through manually tracing the lungs boundary. The segmented lungs obtained through proposed method are compared with the reference standards with the use of various parameters. The proposed method achieved overall overlap ratio of 99.94% accuracy with 0.94 Jaccard’s Index and 0.97 Dice similarity coefficient values https://biomedpharmajournal.org/vol10no4/a-new-hybrid-approach-using-fuzzy-clustering-and-morphological-operations-for-lung-segmentation-in-thoracic-ct-images/ CAD System Computed Tomography Fuzzy c-means Lung Segmentation Thresholding Morphological Operations Pulmonary nodules