A variational level set segmentation formulation based on signal model for images in the presence of intensity inhomogeneity |
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Authors: | Hongzhe Yang Lihui Zhao Songyuan Tang Yongtian Wang |
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Affiliation: | 1. School of Computer Science, Beijing Institute of Technology, Beijing, China;2. School of Electrical Engineering, Liaoning University of Technology, Jinzhou, China;3. School of Optoelectronics, Beijing Institute of Technology, Beijing, China |
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Abstract: | Biological images with significant intensity inhomogeneity are considerably difficult for the tissue segmentation. To overcome the difficulties caused by the intensity inhomogeneity, this study presents a variational level set method to simultaneous bias field estimation and tissue segmentation for images in the presence of intensity inhomogeneity. An energy function is defined in terms of two data fitting terms which incorporate the local clustering properties into the global region information. First, depended on the observed image mode, the local cluster property based on the observed signal is simplified to a criterion function which is similar to the Mumford‐Shah model. The local criterion energy is then integrated with a global region measure, which is based on intensity difference of the true signal. The energy is minimized in a variational level set formulation with a regularity term, thus avoiding the expensive computation of the level set reinitialization and keeping the curve close to the signal distance function. Experiment results on biological images show desirable performance and demonstrate the effectiveness of the proposed algorithm. |
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Keywords: | image segmentation level set bias correction variational method intensity inhomogeneity |
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