Lung segmentation in chest radiographs by means of Gaussian Kernel-based FCM with spatial constraints

Authors:
Zhenghao Shi;Peidong Zhou;Lifeng He;Tsuyoshi Nakamura;Quanzhu Yao;Hidenori Itoh
Affiliations:
School of Computer Science and Engineering, Xi'an University of Technology, Xi'an, China and School of Computer Science and Engineering, Nagoya Institute of Technology, Japan;School of Computer Science and Engineering, Xi'an University of Technology, Xi'an, China;School of Computer Science and Engineering, Nagoya Institute of Technology, Japan and Aichi Prefectural University, Nagakute-cho, Aichi, Japan;School of Computer Science and Engineering, Nagoya Institute of Technology, Japan;School of Computer Science and Engineering, Xi'an University of Technology, Xi'an, China;School of Computer Science and Engineering, Nagoya Institute of Technology, Japan
Venue:
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 3
Year:
2009

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Abstract

A Gaussian kernel-based fuzzy clustering algorithm with spatial constraints for automatic segmentation of lung field in chest radiographs is proposed in this paper. The algorithm is realized by modifying the objective function in the conventional fuzzy c-means algorithm using Gaussian kernel-induced distance metric. The influence of the neighboring pixels on the centre pixel in chest radiograph was also taken into account to make a spatial penalty term. The methods have been tested on a publicly available database of 52 chest radiographs, in which all objects have been manually segmented by a human observer specializing in medical image analysis. Experimental results demonstrate that the proposed method is efficient and effective.