A New Algorithm for Fractal Coding Using Self Organizing Map

Problem statement: In medical imaging, lossy compression schemes are generally not used due to possible loss of useful clinical information and also degradations may result in lossy compression owing to operations like enhancement. As the medical images are huge in size a good lossy compression technology is required to store them in medical archives in an economical manner. There is a need for efficient compression schemes for medical image data. Approach: We had addressed the possibility of using fractal image compression for compressing medical images in our work. We had proposed a novel quasi-losses fractal coding scheme, which would preserve important feature rich portions of the medical image as the domain blocks and generate the remaining part of the image from it using fractal transformations. This study addresses a machine learning based model using SOM to improve the performance and also to reduce the encoding computational complexity. Results: The performance of the proposed algorithm was evaluated in terms of compression ratio, PSNR and encoding computation time, with standard fractal coding for MRI image datasets of size 512×512 over various thresholds. The encoding speed of SOM based proposed algorithm was obtained as 37.17 sec which was very less compared to that achieved in standard fractal image coding algorithm of 1738 sec and also the proposed algorithm improves the PSNR by 2.23 compared to standard fractal algorithm. Conclusion: The results obtained prove that the proposed algorithm outperforms some of the currently existing methods thereby ensuring the possibility of using fractal based image compression algorithms for medical image compression.