Semi-Lossless Fractal MRI Image Compression based on Variable Length Technique

Magnetic Resonance Imaging (MRI) is an advanced technique according to modern medical diagnosis. This mechanism provides a great service to doctors in determining the patient’s condition and if he needs surgical intervention. The data MRI images are characterized by high medical value; therefore, they are high sizes. It is a major obstacle for storing, archiving, sending and receiving data, especially in remote systems. Logically, it has become necessary to find a technology that reduces the redundancy of data while trying to maintain its accuracy (data compression). This paper proposed a Semi-Lossless Fractal Image Compression (SLFIC) model to compress MRI images based on the variable length (Huffman) technique. The newly developed model has demonstrated high-level performance when compared to other compression models using the fractal concept or other technologies. Moreover, the presented model has high accuracy proven through quality metrics: Mean Square Error (MSE) and Peak Signal to Noise Ratio (PSNR).