GPU-based segmentation of dental X-ray images using active contours without edges

Image data is of immense practical importance in medical informatics. In teeth-related radiograph research, the information of teeth shape is the most critical factor for achieving highly automated diagnosis. Automated image segmentation, which aims at automated extraction of region boundary features, plays a fundamental role in understanding image content for searching and mining in medical image. Therefore, accurate segmentation is an essential but difficult task due to low contrast between regions of interest and uneven exposure of the dental X-ray image. To address this problem, several segmentation approaches have been proposed in the literature, with many of them providing rather promising results. In this paper, we will look at a model by Chan-Vese that detects objects not defined by gradient. We will then implement this algorithm on the GPU and see what kind of speedup we can get compared to serial CPU implementations. Finally we will quantity our results as well as make a qualitative evaluation of the method with respect to how it performs for segmenting medical images.