Conference paper
A generalized spatial fuzzy c-means algorithm for medical image segmentation
Medical image segmentation is an indispensable process in viewing and measuring various structures in the brain. However, medical images are inherently low contrast, vague boundaries, and high correlative. The traditional fuzzy c-means (FCM) clustering algorithm considers only the pixel attributes. This leads to accuracy degradation with image segmentation.
To solve this problem, this paper proposes a robust segmentation technique, called a Generalized Spatial Fuzzy C-Means (GSFCM) algorithm, that utilizes both given pixel attributes and the spatial local information which is weighted correspondingly to neighbor elements based on their distance attributes.
This improves the segmentation performance dramatically. Experimental results with several magnetic resonance (MR) images show that the proposed GSFCM algorithm outperforms the traditional FCM algorithms in the various cluster validity functions.
| Language: | English |
|---|---|
| Year: | 2009 |
| Pages: | 409-414 |
| Proceedings: | 2009 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) |
| ISBN: | 142443596X , 142443596x , 1424435978 , 9781424435968 and 9781424435975 |
| ISSN: | 10987584 and 15445615 |
| Types: | Conference paper |
| DOI: | 10.1109/FUZZY.2009.5276878 |
Biomedical imaging Clustering algorithms Image processing Image segmentation Lungs Medical diagnostic imaging Medical treatment Neoplasms Pixel Robustness biomedical MRI fuzzy c-means clustering algorithm fuzzy set theory generalized spatial fuzzy c-means algorithm image segmentation magnetic resonance images medical image processing medical image segmentation pattern clustering spatial local information
