Abstract
This paper presents a parallel image segmentation method based on self-organizing map (SOM) neural network by extending the authors’ former work from serial computation to parallel processing in order to accelerate the computation process. The parallel algorithm is composed of a group of parallel sub-algorithms for implementing the entire segmentation process, including parallel classification of the image into edge/non-edge pattern vectors, parallel training of an SOM network, and parallelly segmenting the image by using the trained SOM model with vector quantization approach. In the paper, the parallel algorithm is implemented on GPU with OpenCL program language and applied to segmenting the human brain MRI images. The experimental results obtained in the work showed that, compared with the original serial algorithm, the parallel algorithm can achieve a significant improvement on the computation efficiency with a speedup ratio of 64.72.
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