Developing Lossy-to-Lossless X-Ray Image Compression Using RoI Based Fuzzy C-means Vector Quantization

Abstract

X-ray image is an important part of a patient’s health history. X-ray image must be store for information retrieval or transmission in the future. The problem for storing and transmitting is the size of x-ray image itself. X-ray images come out with the size of 3 MB or even bigger. Image compression is the answer to overcome storing and transmitting problem. The compression technique must follow several requirements. The first and the most important requirement is that there must be no missing information during the compression process. This is because; the missing information could be a very important part to conduct a diagnosis process. The other requirement is that the compression technique must have the most optimal compression ratio. Lossy-to-lossless compression scheme is introduced to answer the requirements. This compression developed over vector quantization technique. Fuzzy c-means (FCM) is applied to create a set of code vector or codebook. The codebook is used for encoding and decoding process. The encoding process will result an encoded image consist of indexes including in the codebook. Meanwhile, the decoding process will result a reproduction image. The error of this reproduction image is called as image residue. Then, the encoded image and its residue will be store in the image DB or medical image for future use. Lossy-to-lossless scheme make a possibility for physicians to view lossless information over reproduction image (lossy) on a certain region of interest