A New Hybrid Approach Using Fuzzy Clustering and Morphological Operations for Lung Segmentation in Thoracic CT Images

Satya Prakash Sahu¹, Priyanka Agrawal¹, Narendra D Londhe²and ShrishVerma³

¹Departmentof Information Technology, National Institute of Technology, Raipur, India;

²Departmentof Electrical Engineering, National Institute of Technology, Raipur, India;

³Departmentof Electronics and Telecommunication Engineering, National Institute of Technology, Raipur, India;

Corresponding Author E-mail: spsahu.it@nitrr.ac.in.

Abstract: 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

Keywords: CAD System; Computed Tomography;Fuzzy c-means;Lung Segmentation; Thresholding; Morphological Operations Pulmonary nodules;

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