Comparative Assessment of Fractal Image Compression with and without Neural Networks for Medical Images
نویسندگان
چکیده
The Demand for bulk storages of Medical Images in today’s growing world has lead to many advanced Image compression algorithms. One such algorithms used for compressing images is Fractal image Compression (FIC).Fractal Image compression is found effective for medical images in preserving the quality of the image with high Compression Ratio. Fractal Encoding involves partitioning the images into Range Blocks and Domain Blocks and each Range Block is mapped onto the Domain Blocks by using contractive transforms called the Affine Transforms..One of the drawbacks of FIC is that it has long encoding time and short decoding time The encoding time can be minimised by reducing the time taken to search suitable domain blocks for each Range Blocks. This paper is focused in finding a technique to search a suitable domain block through feature extraction method and neural network. To speed up the encoding time an expert system has been trained using Hopfield neural network. In this paper , A comparative analysis of FIC algorithm with neural network and without neural network is being Performed using a set of Magnetic Resonance (MR) images of the Brain, ultrasound Image of Abdomen based on encoding time , Decoding Time, Peak signal To Noise Ratio(PSNR) and Compression Ratio. The Results are simulated using graphical Mat lab and the results of the comparison showed that the Performance of FIC algorithm with neural networks using this proposed technique has improved considerably with regard to encoding time, preserving the quality of the image
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