Different approaches for implementation of Fractal Image Compression on medical images

Image compression applications have been gaining prominence over the years in various fields. Owing to a fast and tensed life style, there is a drastic increase in the number and intensity of diseases which call for intense investigation. The digitization of large medical images like x-ray, Magnetic Resonance (MR) and Computed Tomography (CT) demands large disk storage space. Even with sufficient storage facilities, hospitals face difficulties with regard to storage of data. Numerous image compression methods have been developed to address the problem of storage space and to help the doctors in the analysis of patient's ailments more accurately and reliably. There are different lossless and lossy compression techniques. Joing Photographic Expert Group (JPEG) and Fractal Image Compression (FIC) are examples of lossy compression. FIC is gaining importance in the recent years. FIC is characterized by long encoding time and high Compression Ratio (CR). Further as medical images being voluminous, a high CR is required to reduce the storage space. Fractal Image compression adopts affine transforms to map the range blocks and domain blocks by using the property of self similarity in the images In view of this, the present paper aims in providing a comparative analysis of the different approaches for performing FIC applied on medical images (MR of brain, Mammograms(MG), ultrasound of uterus and CT of bone) to achieve high CR by still retaining the quality of the image. The results have shown a that FIC takes less encoding time when implemented on FPGA than using MATLAB.