An Image Compression Algorithm for Micro-Capsule-Robots to Support Diagnosis in the Body

We propose an image-compression algorithm for micro-capsule robots to support medical diagnosis, treatment, and operations inside the body. For medical purposes, a capsule robot is required to provide doctors with high-quality images without losing important informations for medical decisions. Unfortunately, the power supply to a capsule robot inside the body and the band-width required for communications are extremely limited. Thus, we face trade-offs between power, compression-rate, and quality. To overcome these trade-offs, we analyzed the features of organ images and investigated what kind of information doctors need. The proposed algorithm achived a high-compression-rate with low computational costs without loss of information for doctors that utilized the color features and image frequencies and that were equipped with the region-of-interest mechanism. In comparison with JPEG and JPEG2000, the algorithm realizes a large reduction of computational costs and equivalent in quality concurrently.