Effectiveness of the fractal dimension based classification methods for fractal compression of medical images

Medica1 imaging modalities for different diseases need to be store for accurate and fast quantitative assessment in future reference of the patients. Also, store these medical images in compressed form still a challenging task by maintaining high diagnostic quality. For the higher compression ratio, the reconstructed image quality degrades due to the blocky artifact during the reconstruction process. This paper used the fractal dimension based texture complexity to efficiently classified the domain blocks and reduced the encoding time for the fractal image compression to compress the medical images. The fractal dimension can reduce the blocking artifact by efficiently identifying and separating the complex and smooth blocks. This paper mainly discusses the selection of suitable fractal dimension approach by comparison of domain and range blocks classification using support vector machine, fuzzy c-means and k-means classification approaches.