GPU accelerated fuzzy connected image segmentation by using CUDA.
Image segmentation techniques using fuzzy connectedness principles have shown their effectiveness in segmenting a variety of objects in several large applications in recent years. However, one problem of these algorithms has been their excessive computational requirements when processing large image datasets. Nowadays commodity graphics hardware provides high parallel computing power. In this paper, we present a parallel fuzzy connected image segmentation algorithm on Nvidia's Compute Unified Device Architecture (CUDA) platform for segmenting large medical image data sets. Our experiments based on three data sets with small, medium, and large data size demonstrate the efficiency of the parallel algorithm, which achieves a speed-up factor of 7.2x, 7.3x, and 14.4x, correspondingly, for the three data sets over the sequential implementation of fuzzy connected image segmentation algorithm on CPU.
Zhuge
Ying
Y
Radiation Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA. zhugey@mail.nih.gov
Cao
Yong
Y
Miller
Robert W
RW
D000465:Algorithms; D003196:Computer Graphics; D004867:Equipment Design; D019544:Equipment Failure Analysis; D017143:Fuzzy Logic; D007089:Image Enhancement / Q000295:instrumentation* / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000295:instrumentation* / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted / Q000295:instrumentation*; D013997:Time Factors