Research

An ultra-low-power image compressor for capsule endoscope

Meng-Chun Lin¹ , Lan-Rong Dung¹ and Ping-Kuo Weng²

¹  Department of Electrical and Control Engineering National Chiao Tung University, Hsinchu, Taiwan

²  Solid-State Devices Section, Materials and Electro-Optics Research Division, Chung-Shan Institute of Science and Technology, Lung-Tan, Tao-Yuan, Taiwan

author email corresponding author email

BioMedical Engineering OnLine 2006, 5:14doi:10.1186/1475-925X-5-14

Published:	25 February 2006

Abstract

Background

Gastrointestinal (GI) endoscopy has been popularly applied for the diagnosis of diseases of the alimentary canal including Crohn's Disease, Celiac disease and other malabsorption disorders, benign and malignant tumors of the small intestine, vascular disorders and medication related small bowel injury. The wireless capsule endoscope has been successfully utilized to diagnose diseases of the small intestine and alleviate the discomfort and pain of patients. However, the resolution of demosaicked image is still low, and some interesting spots may be unintentionally omitted. Especially, the images will be severely distorted when physicians zoom images in for detailed diagnosis. Increasing resolution may cause significant power consumption in RF transmitter; hence, image compression is necessary for saving the power dissipation of RF transmitter. To overcome this drawback, we have been developing a new capsule endoscope, called GICam.

Methods

We developed an ultra-low-power image compression processor for capsule endoscope or swallowable imaging capsules. In applications of capsule endoscopy, it is imperative to consider battery life/performance trade-offs. Applying state-of-the-art video compression techniques may significantly reduce the image bit rate by their high compression ratio, but they all require intensive computation and consume much battery power. There are many fast compression algorithms for reducing computation load; however, they may result in distortion of the original image, which is not good for use in the medical care. Thus, this paper will first simplify traditional video compression algorithms and propose a scalable compression architecture.

Conclusion

As the result, the developed video compressor only costs 31 K gates at 2 frames per second, consumes 14.92 mW, and reduces the video size by 75% at least.