Quality assessment of compressed medical images

Abstract

This paper describes the compression of grayscale medical images using set partitioning in hierarchical trees. A novel denoising technique in the wavelet transform domain for multiplicative noise is presented and is used prior to the SPIHT. This algorithm is more effective in terms of the quality, time and computational cost. It maintains the low pass nature of the test image and more effective about the edge preservation. The group of objective measures for SPIHT shows the quality improvement with multiplicative noise reduction. These objective measures may predict the subjective quality of the test images. The SPIHT compression of the medical images achieves the good quality in terms of the SC, SFM, Pratt measure, ESM with profiteering. The results show that under common objective conditions, our compression algorithm can achieve better subjective visual quality, and performs better than that of normal SPIHT in the aspects of compression ratios and coding/decoding time. In this algorithm low pass nature of the medical images due to speckle noise removal and the edges because of the SPIHT are preserved The quality measures like .MSE, PSNR, LMSE, correlation, Pratt's Figure of merit and ESM , NAE are suitable for quality assessment of X-ray images measure.