Abstract
This paper presents a modified Hopfield neural network with fuzzy c-means technique for segmenting multispectral MR brain images. The proposed approach is a novel unsupervised 2-D Hopfield neural network based upon the fuzzy clustering technique, and is suitable for parallel implementation in the application of medical image segmentation. The fuzzy c-means clustering strategy is included in the Hopfield neural network so as to eliminate the need of the weighting factors in the energy function which is formulated and based on a basic concept commonly used in pattern classification, called the `within-class scatter matrix' principle. From the experimental results, it is shown that a near optimal solution can be obtained using the proposed method.
| Original language | English |
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| Pages | 327-330 |
| Number of pages | 4 |
| Publication status | Published - 1996 Dec 1 |
| Event | Proceedings of the 1996 IEEE International Conference on Image Processing, ICIP'96. Part 2 (of 3) - Lausanne, Switz Duration: 1996 Sept 16 → 1996 Sept 19 |
Other
| Other | Proceedings of the 1996 IEEE International Conference on Image Processing, ICIP'96. Part 2 (of 3) |
|---|---|
| City | Lausanne, Switz |
| Period | 96-09-16 → 96-09-19 |
All Science Journal Classification (ASJC) codes
- Hardware and Architecture
- Computer Vision and Pattern Recognition
- Electrical and Electronic Engineering