An Improved Fuzzy Connected Image Segmentation Method Base on CUDA

A paralleled CUDA version of k FOE(CUDA-k FOE)was proposed to segment medical images. CUDA-k FOE achieves fast segmentation when processing large image datasets. However, it cannot precisely handle the competition of edge points when update operations happen by multiple threads simultaneously, thus an iterative correction method to improve CUDA-k FOE was proposed. By analyzing all the pathways of marginal voxels affinity and their consequently caused results, a two iteration correction scheme is employed to achieve the accurate calculation. In these two iterations, the resulted marginal voxels from the first iteration are used as the correction input of the second iteration, therefore, the values of affinity are corrected in the second iteration. Experiments are conducted on three CT image sequences of liver vessels with small, medium, and large size. By choosing three different seed points, final results are not only comparable to the sequential implementation of fuzzy connected image segmentation algorithm on CPU, but achieve more precise calculation compared with CUDA-k FOE.