A Low Power and Real-Time Architecture for Hough Transform Processing Integration in a Full HD-Wireless Capsule Endoscopy

We propose a new paradigm of a smart wireless endoscopic capsule (WCE) that has the ability to select suspicious images containing a polyp before sending them outside the body. To do so, we have designed an image processing system to select images with Regions Of Interest (ROI) containing a polyp. The criterion used to select an ROI is based on the polyp's shape. We use the Hough Transform (HT), a widely used shape-based algorithm for object detection and localization, to make this selection. In this paper, we present a new algorithm to compute in real-time the Hough Transform of high definition images (1920 x 1080 pixels). This algorithm has been designed to be integrated inside a WCE where there are specific constraints: a limited area and a limited amount of energy. To validate our algorithm, we have realized tests using a dataset containing synthetic images, real images, and endoscopic images with polyps. Results have shown that our algorithm is capable to detect circular shapes in synthetic and real images, but also can detect circles with an irregular contour, like that of polyps. We have implemented our architecture and validated it in a Xilinx Spartan 7 FPGA device, with an area of 8 * 8 mm
²
, which is compatible with integration inside a WCE. This architecture runs at 132 MHz with an estimated power consumption of 76 mW and can work close to 10 hours. To improve the capacity of our architecture, we have also made an ASIC estimation, that let our architecture work at 125 MHz, with a power consumption of only 17.2 mW and a duration of approximately 50 hours.