Molecular Image Segmentation Based on Improved Fuzzy Clustering
Segmentation of molecular images is a difficult task due to the low signal-to-noise ratio of images. A novel two-dimensional fuzzy C-means (2DFCM) algorithm is proposed for the molecular image segmentation. The 2DFCM algorithm is composed of three stages. The first stage is the noise suppression by...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Wiley
2007-01-01
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| Series: | International Journal of Biomedical Imaging |
| Online Access: | http://dx.doi.org/10.1155/2007/25182 |
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| _version_ | 1832555085254623232 |
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| author | Jinhua Yu Yuanyuan Wang |
| author_facet | Jinhua Yu Yuanyuan Wang |
| author_sort | Jinhua Yu |
| collection | DOAJ |
| description | Segmentation of molecular images is a difficult task due to the low signal-to-noise ratio of images.
A novel two-dimensional fuzzy C-means (2DFCM) algorithm is proposed for the molecular image
segmentation. The 2DFCM algorithm is composed of three stages. The first stage is the noise
suppression by utilizing a method combining a Gaussian noise filter and anisotropic diffusion
techniques. The second stage is the texture energy characterization using a Gabor wavelet method.
The third stage is introducing spatial constraints provided by the denoising data and the textural
information into the two-dimensional fuzzy clustering. The incorporation of intensity and textural
information allows the 2DFCM algorithm to produce satisfactory segmentation results for images
corrupted by noise (outliers) and intensity variations. The 2DFCM can achieve 0.96±0.03 segmentation accuracy for synthetic images under different imaging conditions. Experimental results on a real molecular image also show the effectiveness of the proposed algorithm. |
| format | Article |
| id | doaj-art-f0532660afcc4de4be5a10fc0623ae07 |
| institution | Kabale University |
| issn | 1687-4188 1687-4196 |
| language | English |
| publishDate | 2007-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Biomedical Imaging |
| spelling | doaj-art-f0532660afcc4de4be5a10fc0623ae072025-02-03T05:49:43ZengWileyInternational Journal of Biomedical Imaging1687-41881687-41962007-01-01200710.1155/2007/2518225182Molecular Image Segmentation Based on Improved Fuzzy ClusteringJinhua Yu0Yuanyuan Wang1Department of Electronic Engineering, Fudan University, Shanghai 200433, ChinaDepartment of Electronic Engineering, Fudan University, Shanghai 200433, ChinaSegmentation of molecular images is a difficult task due to the low signal-to-noise ratio of images. A novel two-dimensional fuzzy C-means (2DFCM) algorithm is proposed for the molecular image segmentation. The 2DFCM algorithm is composed of three stages. The first stage is the noise suppression by utilizing a method combining a Gaussian noise filter and anisotropic diffusion techniques. The second stage is the texture energy characterization using a Gabor wavelet method. The third stage is introducing spatial constraints provided by the denoising data and the textural information into the two-dimensional fuzzy clustering. The incorporation of intensity and textural information allows the 2DFCM algorithm to produce satisfactory segmentation results for images corrupted by noise (outliers) and intensity variations. The 2DFCM can achieve 0.96±0.03 segmentation accuracy for synthetic images under different imaging conditions. Experimental results on a real molecular image also show the effectiveness of the proposed algorithm.http://dx.doi.org/10.1155/2007/25182 |
| spellingShingle | Jinhua Yu Yuanyuan Wang Molecular Image Segmentation Based on Improved Fuzzy Clustering International Journal of Biomedical Imaging |
| title | Molecular Image Segmentation Based on Improved Fuzzy Clustering |
| title_full | Molecular Image Segmentation Based on Improved Fuzzy Clustering |
| title_fullStr | Molecular Image Segmentation Based on Improved Fuzzy Clustering |
| title_full_unstemmed | Molecular Image Segmentation Based on Improved Fuzzy Clustering |
| title_short | Molecular Image Segmentation Based on Improved Fuzzy Clustering |
| title_sort | molecular image segmentation based on improved fuzzy clustering |
| url | http://dx.doi.org/10.1155/2007/25182 |
| work_keys_str_mv | AT jinhuayu molecularimagesegmentationbasedonimprovedfuzzyclustering AT yuanyuanwang molecularimagesegmentationbasedonimprovedfuzzyclustering |