Semisupervised Soft Mumford-Shah Model for MRI Brain Image Segmentation
One challenge of unsupervised MRI brain image segmentation is the central gray matter due to the faint contrast with respect to the surrounding white matter. In this paper, the necessity of supervised image segmentation is addressed, and a soft Mumford-Shah model is introduced. Then, a framework of...
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| Format: | Article |
| Language: | English |
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Wiley
2016-01-01
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| Series: | Applied Computational Intelligence and Soft Computing |
| Online Access: | http://dx.doi.org/10.1155/2016/8508329 |
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| author | Hong-Yuan Wang Fuhua Chen |
| author_facet | Hong-Yuan Wang Fuhua Chen |
| author_sort | Hong-Yuan Wang |
| collection | DOAJ |
| description | One challenge of unsupervised MRI brain image segmentation is the central gray matter due to the faint contrast with respect to the surrounding white matter. In this paper, the necessity of supervised image segmentation is addressed, and a soft Mumford-Shah model is introduced. Then, a framework of semisupervised image segmentation based on soft Mumford-Shah model is developed. The main contribution of this paper lies in the development a framework of a semisupervised soft image segmentation using both Bayesian principle and the principle of soft image segmentation. The developed framework classifies pixels using a semisupervised and interactive way, where the class of a pixel is not only determined by its features but also determined by its distance from those known regions. The developed semisupervised soft segmentation model turns out to be an extension of the unsupervised soft Mumford-Shah model. The framework is then applied to MRI brain image segmentation. Experimental results demonstrate that the developed framework outperforms the state-of-the-art methods of unsupervised segmentation. The new method can produce segmentation as precise as required. |
| format | Article |
| id | doaj-art-8c466f4c00504ee2a4423a6c9a4dd7ae |
| institution | Kabale University |
| issn | 1687-9724 1687-9732 |
| language | English |
| publishDate | 2016-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Applied Computational Intelligence and Soft Computing |
| spelling | doaj-art-8c466f4c00504ee2a4423a6c9a4dd7ae2025-08-20T03:37:41ZengWileyApplied Computational Intelligence and Soft Computing1687-97241687-97322016-01-01201610.1155/2016/85083298508329Semisupervised Soft Mumford-Shah Model for MRI Brain Image SegmentationHong-Yuan Wang0Fuhua Chen1School of Information Science & Engineering, Changzhou University, Changzhou 213164, ChinaDepartment of Natural Science & Mathematics, West Liberty University, West Liberty, WV 26074, USAOne challenge of unsupervised MRI brain image segmentation is the central gray matter due to the faint contrast with respect to the surrounding white matter. In this paper, the necessity of supervised image segmentation is addressed, and a soft Mumford-Shah model is introduced. Then, a framework of semisupervised image segmentation based on soft Mumford-Shah model is developed. The main contribution of this paper lies in the development a framework of a semisupervised soft image segmentation using both Bayesian principle and the principle of soft image segmentation. The developed framework classifies pixels using a semisupervised and interactive way, where the class of a pixel is not only determined by its features but also determined by its distance from those known regions. The developed semisupervised soft segmentation model turns out to be an extension of the unsupervised soft Mumford-Shah model. The framework is then applied to MRI brain image segmentation. Experimental results demonstrate that the developed framework outperforms the state-of-the-art methods of unsupervised segmentation. The new method can produce segmentation as precise as required.http://dx.doi.org/10.1155/2016/8508329 |
| spellingShingle | Hong-Yuan Wang Fuhua Chen Semisupervised Soft Mumford-Shah Model for MRI Brain Image Segmentation Applied Computational Intelligence and Soft Computing |
| title | Semisupervised Soft Mumford-Shah Model for MRI Brain Image Segmentation |
| title_full | Semisupervised Soft Mumford-Shah Model for MRI Brain Image Segmentation |
| title_fullStr | Semisupervised Soft Mumford-Shah Model for MRI Brain Image Segmentation |
| title_full_unstemmed | Semisupervised Soft Mumford-Shah Model for MRI Brain Image Segmentation |
| title_short | Semisupervised Soft Mumford-Shah Model for MRI Brain Image Segmentation |
| title_sort | semisupervised soft mumford shah model for mri brain image segmentation |
| url | http://dx.doi.org/10.1155/2016/8508329 |
| work_keys_str_mv | AT hongyuanwang semisupervisedsoftmumfordshahmodelformribrainimagesegmentation AT fuhuachen semisupervisedsoftmumfordshahmodelformribrainimagesegmentation |