Performance of Fractal Image Compression for Medical Images: A Comprehensive Literature Review

Abstract

Generally the fractal image compression is a technique based on the presentation of an image by a contractive transform, on the space of image, for which the fixed point is close to the original image. The fractal image compression is rapidly growing principle covers a wide variety of coding scheme in various domains. A large number of theoretical aspects of this concept are available. However, a few concentrations have been given to the image encoding model which justifies its proper implementation. Most fractal base schemes are not competitive with the current state of the art like JPEG, JPEG2000. To identify the performance of fractal image compression, specifically for the medical images, a comprehensive review of worldwide contributions from 1990 to 2013 has been carried out. As a result, the fractal image compression techniques are classified into four domains, i.e., spatial, frequency, soft computing, and hybrid. It is found that, fractal theory in spatial domain, fractal theory along with discrete cosine transform and wavelet transform in frequency domain, fractal theory along with fuzzy logic and neural network in soft computing domain, and combination of fractal theory along with discrete cosine transform, wavelet transform, fuzzy logic and neural network in hybrid domain are often used by the contributors. It is found that the fractal dimension and fuzzy logic approach on regular image processing are sufficiently suitable for image texture analysis. Consequently fuzzy logic is successfully applied in regular image and found outstanding results. On the basis of these facts it is anticipated that the fractal dimension and fuzzy logic could be a suitable approach in medical image compression using fractal theory as a future expansion is discussed in this review article.