A GPU Accelerated Algorithm for Blood Detection inWireless Capsule Endoscopy Images

Abstract

Wireless capsule endoscopy (WCE) has emerged as a powerful tool in the diagnosis of small intestine diseases. One of the main limiting factors is that it produces a huge number of images, whose analysis, to be done by a doctor, is an extremely time consuming process. Recently, we proposed (Figueiredo et al. An automatic blood detection algorithm for wireless capsule endoscopy images. In: Computational Vision and Medical Image Processing IV: VIPIMAGE 2013, pp. 237–241. Madeira Island, Funchal, Portugal (2013)) a computer-aided diagnosis system for blood detection in WCE images. While the algorithm in (Figueiredo et al. An automatic blood detection algorithm for wireless capsule endoscopy images. In: Computational Vision and Medical Image Processing IV: VIPIMAGE 2013, pp. 237–241. Madeira Island, Funchal, Portugal (2013)) is very promising in classifying the WCE images, it still does not serve the purpose of doing the analysis within a very less stipulated amount of time; however, the algorithm can indeed profit from a parallelized implementation. In the algorithm we identified two crucial steps, segmentation (for discarding non-informative regions in the image that can interfere with the blood detection) and the construction of an appropriate blood detector function, as being responsible for taking most of the global processing time. In this work, a suitable GPU-based (graphics processing unit) framework is proposed for speeding up the segmentation and blood detection execution times. Experiments show that the accelerated procedure is on average 50 times faster than the original one, and is able of processing 72 frames per second.