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Article type: Research Article
Authors: Li, Linsheng; | Zeng, Li; | Qiu, Changjun | Liu, Linghui
Affiliations: ICT Research Center, Key Laboratory of Optoelectronic Technology and System of the Education Ministry of China, Chongqing University, Chongqing, China | College of Mechanical Engineering, University of South China, Hengyang, Hunan, China | College of Mathematics and Statistics, Chongqing University, Chongqing, China
Note: [] Corresponding author: Li Zeng, ICT Research Center, Key Laboratory of Optoelectronic Technology and System of the Education Ministry of China, Chongqing University, Chongqing 400044, China. Tel.: +86 23 8639 4828; Fax: +86 23 6510 3562; E-mail: drlizeng@hotmail.com
Abstract: Intensity inhomogeneity may cause considerable difficulties in segmentation of CT image. In order to overcome the difficulties caused by intensity inhomogeneity, the region-scalable fitting (RSF) model was put forward. RSF model draws upon intensity information in local regions with a controllable scale. But only using intensity information may lead to slow convergence rate and poor denoise ability. Combining the method of robust statistics, RSF model is improved in this paper. In the improved model, the intensity in RSF model is replaced with local robust statistics which is the weighted combination of inter-quartile range, mean absolute deviation and intensity median in local region. Inter-quartile range and mean absolute deviation in local region are introduced to sharpen object boundaries, and intensity median in local region is introduced to reduce image noise. The contrast experiments between RSF model and the improved model are provided, which demonstrate the fast convergence rate and robustness to noise of the improved model.
Keywords: Image segmentation, intensity inhomogeneity, computer tomography (CT), robust statistics, region-scalable fitting (RSF)
DOI: 10.3233/XST-2012-0334
Journal: Journal of X-Ray Science and Technology, vol. 20, no. 3, pp. 255-267, 2012
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