Design of a Lossless Image Compression System for Video Capsule Endoscopy and Its Performance in In-Vivo Trials
AbstractIn this paper, a new low complexity and lossless image compression system for capsule endoscopy (CE) is presented. The compressor consists of a low-cost YEF color space converter and variable-length predictive with a combination of Golomb-Rice and unary encoding. All these components have been heavily optimized for low-power and low-cost and lossless in nature. As a result, the entire compression system does not incur any loss of image information. Unlike transform based algorithms, the compressor can be interfaced with commercial image sensors which send pixel data in raster-scan fashion that eliminates the need of having large buffer memory. The compression algorithm is capable to work with white light imaging (WLI) and narrow band imaging (NBI) with average compression ratio of 78% and 84% respectively. Finally, a complete capsule endoscopy system is developed on a single, low-power, 65-nm field programmable gate arrays (FPGA) chip. The prototype is developed using circular PCBs having a diameter of 16 mm. Several in-vivo and ex-vivo trials using pig's intestine have been conducted using the prototype to validate the performance of the proposed lossless compression algorithm. The results show that, compared with all other existing works, the proposed algorithm offers a solution to wireless capsule endoscopy with lossless and yet acceptable level of compression. View Full-Text
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Khan, T.H.; Wahid, K.A. Design of a Lossless Image Compression System for Video Capsule Endoscopy and Its Performance in In-Vivo Trials. Sensors 2014, 14, 20779-20799.
Khan TH, Wahid KA. Design of a Lossless Image Compression System for Video Capsule Endoscopy and Its Performance in In-Vivo Trials. Sensors. 2014; 14(11):20779-20799.Chicago/Turabian Style
Khan, Tareq H.; Wahid, Khan A. 2014. "Design of a Lossless Image Compression System for Video Capsule Endoscopy and Its Performance in In-Vivo Trials." Sensors 14, no. 11: 20779-20799.