GPU-Based Fuzzy C-Means Clustering Algorithm for Image Segmentation
This provides a faster method for medical image segmentation, particularly for brain tissue analysis, but it is incremental as it applies existing parallelization techniques to a known algorithm.
The paper tackled the problem of slow image segmentation using the Fuzzy C-Means clustering algorithm by developing a GPU-based implementation, achieving a 245-fold speedup with execution times reduced from 519 seconds to 2.33 seconds for a 1MB dataset.
In this paper, a fast and practical GPU-based implementation of Fuzzy C-Means(FCM) clustering algorithm for image segmentation is proposed. First, an extensive analysis is conducted to study the dependency among the image pixels in the algorithm for parallelization. The proposed GPU-based FCM has been tested on digital brain simulated dataset to segment white matter(WM), gray matter(GM) and cerebrospinal fluid (CSF) soft tissue regions. The execution time of the sequential FCM is 519 seconds for an image dataset with the size of 1MB. While the proposed GPU-based FCM requires only 2.33 seconds for the similar size of image dataset. An estimated 245-fold speedup is measured for the data size of 40 KB on a CUDA device that has 448 processors.