Medical Image Compression Based on Daubechies Wavelet, Global Thresholding and Huffman Encoding Algorithm

Due to the advent of medical image modalities such as X-ray angiography, CT imaging, MRI, ultrasound and digital video, a large volume of image data is being generated in hospitals and medical organizations nowadays. One of the hurdles faced by the health care institutions is limited network bandwidth to access, transfer and share these medical images for the teleconsultation, telediagnosis and telemedicine for the primary diagnostic purposes. Image compression gives the best options for reducing the cost effective delivery of medical images across the globe. Wavelet based image compression is the fundamental block in JPEG-2000 standard due to its good characteristics and multiresolution. The aim of this study is to identify the correlation between level of decomposition of the wavelet filter and quality of the reconstructed image. To investigate this, the most popularly referred to in the literature Daubechies wavelet (db2) filter is used for multilevel decomposition on a selected set of medical images and then global thresholding is applied for quantization. The quantized values are encoded through Huffman variable entropy coding technique. The results of this investigation are presented in this paper. The simulation results show that the proposed algorithm gives the better performance and useful for the developers for identifying the right or most appropriate level of decomposition of wavelet filter for medical image compression.