A Fuzzy Backpropagation Approach to Biomedical Image Compression

Abdul Khader Jilani Saudagar1 and Abdul Sattar Syed2

1College of Computers and Information Technology, University of Tabuk, Tabuk 71431, Kingdom of Saudi Arabia.

2Royal Institute of Technology and Science, Chevella, R.R. Dist, Andhra Pradesh 501503, India.


Biomedical image compression plays a vital role in today’s communication. The limitation in allocated bandwidth leads to slower communication. To exchange the rate of transmission in the limited bandwidth the biomedical image data must be compressed before transmission without losing the clinical information content. JPEG, JPEG2000 image compression system follows huffman coding for image compression. JPEG 2000 coding system use wavelet transform, which decompose the image into different levels, where the coefficient in each sub band are uncorrelated from coefficient of other sub bands. Embedded Zero tree Wavelet (EZW) coding exploits the multi-resolution properties of the wavelet transform to give a computationally simple algorithm with better performance compared to existing wavelet transforms. For further improvement of compression applications other coding methods were recently been suggested. A fuzzy optimization design based on neural networks is presented as a new method of biomedical image processing. The combination system adopts a new fuzzy neuron network (FNN) which can appropriately adjust input, output values and increases robustness, stability and working speed of the network by achieving high compression ratio. The performance analysis of different biomedical images is proposed with an analysis of EZW coding system with Fuzzy Backpropagation algorithm. The implementation and analysis shows approximately 20% more accuracy in retrieved image compare to the existing EZW coding system.

Keywords: Accuracy, Compression, EZW, FNN, JPEG2000, Performance.

     Back to TOC