Localized FCM Clustering with Spatial Information for Medical Image Segmentation and Bias Field Estimation
This paper presents a novel fuzzy energy minimization method for simultaneous segmentation and bias field estimation of medical images. We first define an objective function based on a localized fuzzy c-means (FCM) clustering for the image intensities in a neighborhood around each point. Then, this...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2013-01-01
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| Series: | International Journal of Biomedical Imaging |
| Online Access: | http://dx.doi.org/10.1155/2013/930301 |
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| _version_ | 1832545391814377472 |
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| author | Wenchao Cui Yi Wang Yangyu Fan Yan Feng Tao Lei |
| author_facet | Wenchao Cui Yi Wang Yangyu Fan Yan Feng Tao Lei |
| author_sort | Wenchao Cui |
| collection | DOAJ |
| description | This paper presents a novel fuzzy energy minimization method for simultaneous segmentation and bias field estimation of medical images. We first define an objective function based on a localized fuzzy c-means (FCM) clustering for the image intensities in a neighborhood around each point. Then, this objective function is integrated with respect to the neighborhood center over the entire image domain to formulate a global fuzzy energy, which depends on membership functions, a bias field that accounts for the intensity inhomogeneity, and the constants that approximate the true intensities of the corresponding tissues. Therefore, segmentation and bias field estimation are simultaneously achieved by minimizing the global fuzzy energy. Besides, to reduce the impact of noise, the proposed algorithm incorporates spatial information into the membership function using the spatial function which is the summation of the membership functions in the neighborhood of each pixel under consideration. Experimental results on synthetic and real images are given to demonstrate the desirable performance of the proposed algorithm. |
| format | Article |
| id | doaj-art-aaf8ba774110450bb12c7df2e6b45943 |
| institution | Kabale University |
| issn | 1687-4188 1687-4196 |
| language | English |
| publishDate | 2013-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Biomedical Imaging |
| spelling | doaj-art-aaf8ba774110450bb12c7df2e6b459432025-02-03T07:26:00ZengWileyInternational Journal of Biomedical Imaging1687-41881687-41962013-01-01201310.1155/2013/930301930301Localized FCM Clustering with Spatial Information for Medical Image Segmentation and Bias Field EstimationWenchao Cui0Yi Wang1Yangyu Fan2Yan Feng3Tao Lei4School of Electronics and Information, Northwestern Polytechnical University, Xi'an 710072, ChinaSchool of Electronics and Information, Northwestern Polytechnical University, Xi'an 710072, ChinaSchool of Electronics and Information, Northwestern Polytechnical University, Xi'an 710072, ChinaSchool of Electronics and Information, Northwestern Polytechnical University, Xi'an 710072, ChinaSchool of Electronics and Information, Northwestern Polytechnical University, Xi'an 710072, ChinaThis paper presents a novel fuzzy energy minimization method for simultaneous segmentation and bias field estimation of medical images. We first define an objective function based on a localized fuzzy c-means (FCM) clustering for the image intensities in a neighborhood around each point. Then, this objective function is integrated with respect to the neighborhood center over the entire image domain to formulate a global fuzzy energy, which depends on membership functions, a bias field that accounts for the intensity inhomogeneity, and the constants that approximate the true intensities of the corresponding tissues. Therefore, segmentation and bias field estimation are simultaneously achieved by minimizing the global fuzzy energy. Besides, to reduce the impact of noise, the proposed algorithm incorporates spatial information into the membership function using the spatial function which is the summation of the membership functions in the neighborhood of each pixel under consideration. Experimental results on synthetic and real images are given to demonstrate the desirable performance of the proposed algorithm.http://dx.doi.org/10.1155/2013/930301 |
| spellingShingle | Wenchao Cui Yi Wang Yangyu Fan Yan Feng Tao Lei Localized FCM Clustering with Spatial Information for Medical Image Segmentation and Bias Field Estimation International Journal of Biomedical Imaging |
| title | Localized FCM Clustering with Spatial Information for Medical Image Segmentation and Bias Field Estimation |
| title_full | Localized FCM Clustering with Spatial Information for Medical Image Segmentation and Bias Field Estimation |
| title_fullStr | Localized FCM Clustering with Spatial Information for Medical Image Segmentation and Bias Field Estimation |
| title_full_unstemmed | Localized FCM Clustering with Spatial Information for Medical Image Segmentation and Bias Field Estimation |
| title_short | Localized FCM Clustering with Spatial Information for Medical Image Segmentation and Bias Field Estimation |
| title_sort | localized fcm clustering with spatial information for medical image segmentation and bias field estimation |
| url | http://dx.doi.org/10.1155/2013/930301 |
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