Hybrid Self Organizing Map for Improved Implementation of Brain MRI Segmentation
| Autor: | T. Logeswari, M. Karnan |
|---|---|
| Rok vydání: | 2010 |
| Předmět: |
Segmentation-based object categorization
business.industry Computer science ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Scale-space segmentation Pattern recognition Image segmentation Minimum spanning tree-based segmentation Image texture Region growing Segmentation Computer vision Artificial intelligence Cluster analysis business |
| Zdroj: | ICSAP |
| DOI: | 10.1109/icsap.2010.56 |
| Popis: | Image segmentation denotes a process of partitioning an image into distinct regions. A large variety of different segmentation approaches for images have been developed. Among them, the clustering methods have been extensively investigated and used. In this paper, a clustering based approach using a Self Organizing Map (SOM) algorithm is proposed for medical image segmentation. This paper describe segmentation method consists of two phases. In the first phase, the MRI brain image is acquired from patient database. In that film artifact and noise are removed. In the second phase (MR) image segmentation is to accurately identify the principal tissue structures in these image volumes. A new unsupervised MR image segmentation method based on fuzzy C-Mean clustering algorithm for the Segmentation is presented |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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