Comparisons of multi-resolution analysis methods for mammogram and fingerprint image compression

Summary form only given. This study presents a performance comparison analysis of Fourier transform (FT), discrete cosine transform (DCT), wavelet transform (WT), and wavelet packets (WP) on a 1024×1024, 12-bit mammogram and a 512×256, 8-bit, fingerprint image. In the multi-resolution analysis methods, three to five level decompositions and different entropy models at any decomposition levels will be used. An adaptable signal decomposition algorithm for minimizing the decomposition tree will also be introduced. The images are first segmented into two regions: region of interest (ex. micro-calcification in the mammograms), and the background region. The two regions are then compressed at two different levels, to better preserve the information in the image, but most importantly in the region of interest. The quality of the resultant compressed images is subjected to visual analysis by a group of 30 non-experts students, as well as analyzed objectively based on the peak signal-to-noise ratio (PSNR), mean square error (MSE), and reconstruction error. This study could potentially help radiologists and fingerprint experts better detect the important details in the images. Furthermore, the results will save storage space, reduce access time, and improve the accuracy of diagnosis - in other words, cost savings. The compressed images are also better suited for remote access and transfer, for tele-diagnostic, and tele-medicine research and training.