29 August 2016 Local kernel mapping based piecewise constant model for medical image segmentation
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Proceedings Volume 10033, Eighth International Conference on Digital Image Processing (ICDIP 2016); 100331O (2016) https://doi.org/10.1117/12.2244918
Event: Eighth International Conference on Digital Image Processing (ICDIP 2016), 2016, Chengu, China
Abstract
Intensity inhomogeneity and noise are two major obstacles for segmenting medical images. The global kernel mapping based piecewise constant model (PCM) has superior performance on resisting noise, though it fails to cope with intensity inhomogeneity. In order to overcome the difficulty caused by intensity inhomogeneity, we first establish an energy based on kernel mapping in a neighborhood of a pixel. Then such energies for all pixels in an image are integrated to formulate the energy of the local kernel mapping based PCM. Energy minimization has been implemented in level set framework. Comparative experimental results show that our proposed model has higher segmentation accuracy in the presence of intensity inhomogeneity and noise.
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Wenchao Cui, Jian He, Guoqiang Gong, Ke Lu, Shuifa Sun, "Local kernel mapping based piecewise constant model for medical image segmentation", Proc. SPIE 10033, Eighth International Conference on Digital Image Processing (ICDIP 2016), 100331O (29 August 2016); doi: 10.1117/12.2244918; https://doi.org/10.1117/12.2244918
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