Segmentation of colon tissue sample images using multiple graphics accelerators

Abstract

Nowadays, processing medical images is increasingly done through using digital imagery and custom software solutions. The distributed algorithm presented in this paper is used to detect special tissue parts, the nuclei on haematoxylin and eosin stained colon tissue sample images. The main aim of this work is the development of a new data-parallel region growing algorithm that can be implemented even in an environment using multiple video accelerators. This new method has three levels of parallelism: (a) the parallel region growing itself, (b) starting more region growing in the device, and (c) using more than one accelerator. We use the split-and-merge technique based on our already existing data-parallel cell nuclei segmentation algorithm extended with a fast, backtracking-based, non-overlapping cell filter method. This extension does not cause significant degradation of the accuracy; the results are practically the same as those of the original sequential region growing method. However, as expected, using more devices usually means that less time is needed to process the tissue image; in the case of the configuration of one central processing unit and two graphics cards, the average speed-up is about 4-6×. The implemented algorithm has the additional advantage of efficiently processing very large images with high memory requirements.

Keywords: CUDA; Cell nuclei detection; Data parallel algorithm; Distributed algorithm; GPGPU; Medical image segmentation.