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Recently, the wavelet transform has emerged as a cutting edge technology within the field of image compression research. Wavelet methods involve overlapping transforms with varying-length basis functions. This overlapping nature of the transform alleviates blocking artifacts, while the multi-resolution character of the wavelet decomposition leads to superior energy compaction and perceptual quality of the decompressed image. Embedded zerotree wavelet (EZW) coder is the first algorithm to show the full power of wavelet-based image compression. The main purpose of this paper is to investigate the impact and quality of orthogonal wavelet filter in compressing medical image by using EZW. Meanwhile, we also look into the effect of the level of wavelet decomposition towards compression efficiency. The wavelet filters used are Haar and Daubechies. The compression simulations are done on three modalities of medical images. The objective (based on PSNR) and subjective (perceived image quality) results of these simulations are presented. [ABSTRACT FROM AUTHOR]

Recently, the wavelet transform has emerged as a cutting edge technology, within the field of image compression research. The basis functions of the wavelet transform are known as wavelets. There are a variety of different wavelet functions to suit the needs of different applications. Among the most popular wavelets are Haar, Daubechies, Coiflet and Biorthogonal, etc. The best wavelets (functions) for medical image compression axe widely unknown. The purpose of this paper is to examine and compare the difference in impact and quality of a set of wavelet functions (wavelets) to image quality for implementation in a digitized still medical image compression with different modalities. We used two approaches to the measurement of medical image quality: objectively, using peak signal to noise ratio (PSNR) and subjectively, using perceived image quality. Finally, we defined an optimal wavelet filter for each modality of medical image. [ABSTRACT FROM AUTHOR]

Due to the constrained bandwidth and storage capacity, medical images must be compressed before transmission and storage. However, compression will reduce image fidelity, especially when the image is compressed of lower bit rate, which cannot be tolerated in medical field. In this paper, the compression performance of the new JPEG-2000 and the more conventional JPEG is studied. The parameters used for comparison include the compression efficiency, peak signal-to-noise ratio (PSNR), picture quality scale (PQS), and mean opinion score (MOS). Three types of medical images are used — X-ray, magnetic resonance imaging (MRI) and ultrasound. Overall, the study shows that JPEG-2000 compression is more acceptable and superior compare to JPEG for lossy compression. [ABSTRACT FROM AUTHOR]