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 [Formula: see text], 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.

Toward embedded detection of polyps in WCE images for early diagnosis of colorectal cancer.

PURPOSE: Wireless capsule endoscopy (WCE) is commonly used for noninvasive gastrointestinal tract evaluation, including the detection of mucosal polyps. A new embeddable method for polyp detection in wireless capsule endoscopic images was developed and tested. METHODS: First, possible polyps within the image were extracted using geometric shape features. Next, the candidate regions of interest were evaluated with a boosting based method using textural features. Each step was carefully chosen to accommodate hardware implementation constraints. The method's performance was evaluated on WCE datasets including 300 images with polyps and 1,200 images without polyps. Hardware implementation of the proposed approach was evaluated to quantitatively demonstrate the feasibility of such integration into the WCE itself. RESULTS: The boosting based polyp classification demonstrated a sensitivity of 91.0 %, a specificity of 95.2 % and a false detection rate of 4.8 %. This performance is close to that reported recently in systems developed for an online analysis of video colonoscopy images. CONCLUSION: A new method for polyp detection in videoendoscopic WCE examinations was developed using boosting based approach. This method achieved good classification performance and can be implemented in situ with embedded hardware.

Multi-objective approach for energy-aware workflow scheduling in cloud computing environments.

We address the problem of scheduling workflow applications on heterogeneous computing systems like cloud computing infrastructures. In general, the cloud workflow scheduling is a complex optimization problem which requires considering different criteria so as to meet a large number of QoS (Quality of Service) requirements. Traditional research in workflow scheduling mainly focuses on the optimization constrained by time or cost without paying attention to energy consumption. The main contribution of this study is to propose a new approach for multi-objective workflow scheduling in clouds, and present the hybrid PSO algorithm to optimize the scheduling performance. Our method is based on the Dynamic Voltage and Frequency Scaling (DVFS) technique to minimize energy consumption. This technique allows processors to operate in different voltage supply levels by sacrificing clock frequencies. This multiple voltage involves a compromise between the quality of schedules and energy. Simulation results on synthetic and real-world scientific applications highlight the robust performance of the proposed approach.

Prototype of video endoscopic capsule with 3-d imaging capabilities.

Wireless video capsules can now carry out gastroenterological examinations. The images make it possible to analyze some diseases during postexamination, but the gastroenterologist could make a direct diagnosis if the video capsule integrated vision algorithms. The first step toward in situ diagnosis is the implementation of 3-D imaging techniques in the video capsule. By transmitting only the diagnosis instead of the images, the video capsule autonomy is increased. This paper focuses on the Cyclope project, an embedded active vision system that is able to provide 3-D and texture data in real time. The challenge is to realize this integrated sensor with constraints on size, consumption, and processing, which are inherent limitations of the video capsule. We present the hardware and software development of a wireless multispectral vision sensor which enables the transmission of the 3-D reconstruction of a scene in real time. An FPGA-based prototype has been designed to show the proof of concept. Experiments in the laboratory, in vitro, and in vivo on a pig have been performed to determine the performance of the 3-D vision system. A roadmap towardthe integrated system is set out.