A parallel adaptive segmentation method based on SOM and GPU with application to MRI image processing

Abstract

In this paper we develop a set of parallel algorithms for image segmentation basing on the authors׳ former work, the self-organizing map (SOM) based vector quantization (VQ) approach, by extending the method from serial computation to parallel processing, in order to accelerate the computation process. The parallel segmentation scheme is composed of a group of parallel algorithms for implementing the whole segmentation process, including parallel classification of the image into edge and non-edge pattern vectors, parallel training of an SOM network, parallel segmentation of the image by using the trained SOM model with VQ method, and adaptive parallel estimation of the segment number of the image being processed. In the paper, all the parallel algorithms have been implemented on graphic processing units (GPU) and applied to segmenting the human brain MRI images. The experimental results obtained in the work show that, compared with the original serial method implemented on CPU, the proposed parallel approach can achieve a significant improvement on the computation efficiency with overall speedup ratios increasing from 28.81 to 89.12 as image sizes increasing from 128×128 to 1024×1024, while keeping the segmentation performance unchanged.