RESEARCH PAPER International Journal of Recent Trends in Engineering, Vol 1, No. 3, May 2009 97 Wavelet Based Medical Image Compression Using ROI EZW D.VIJENDRA BABU 1 , Dr.N.R.ALAMELU 2 1 Asst.Proffesor &Head, Department of Electronics and Communication Engineering & Biomedical Engineering, Aarupadai Veedu Institute of Technology, Vinayaka Missions University, Chennai, India. sirdvijendrababu@yahoo.co.in 2 Principal, Aarupadai Veedu Institute of Technology, Vinayaka Missions University, Chennai, India. nra@vinayakamissions.com Abstract— This paper presents an approach for an Enhanced Image Compression Method using Partial EZW Algorithm. This is based on the progressive image compression algorithm, EZW which is an extension of Shapiro’s embedded Zero tree Wavelet Algorithm. The proposed Partial EZW Algorithm overcomes the difficulty of EZW that loses its efficiency in transmitting lower bit planes. In this paper, we include integer wavelet transformation and region of interest coding to Partial EZW and hence make it more superior to EZW and SPIHT Algorithm and it is proved with the results. Index Terms— Embedded zero wavelet, Region of interest, wavelet, SPIHT, compression I. INTRODUCTION Increasingly, medical images are acquired and stored digitally. These images may be very large in size and number and compression offers a means to reduce the cost of storage and increase the speed of transmission. Image compression is minimizing the size in bytes of a graphics file without degrading the quality of the image. The resolution in file size allows more images to be stored in a given amount of disk or memory space. It also reduces the time required for images to be sent over the Internet or download from WebPages. Several compression algorithms were developed. J.M. Shapiro developed the embedded zero tree wavelet algorithm in [7] which yields a fully embedded code and consistent compression. With embedded coding, it is possible to recover the lossy version with distortion corresponding to the rate of the received image at the point of decoding process. EZW is a progressive image compression algorithm. As quoted in [4] EZW is found to have the drawback that the compression decreases during the transmission of least significant bits. This paper proposes an Enhanced Partial EZW algorithm that is based on the probability of significant coefficients within each bit plane and it also includes integer wavelet transform and region of interest coding, ie., ROI- IWT(Region Of Interest – Integer Wavelet Transform) and thereby improves the performance. The organization of this paper is as follows. An overview of EZW algorithm is given in Section 2. Section 3 discusses on Integer Wavelet Transform. In Section 4, the detail of the proposed method is presented. The proposed method is tested and simulation results are illustrated along with discussions in Section 5. Finally, the conclusions are given in Section 6. II. OVERVIEW OF EZW CODING ALGORITHM A simple block diagram of image compression system is shown in Fig. 1. Fig 2.1 Block diagram of Image compression system One of the most important characteristics of DWT is multiresolution decomposition. An image decomposed by wavelet transform can be reconstructed with desired resolution.When first level 2D DWT is applied to an image, it forms four transform coefficients.The first letter corresponds to applying either low pass or high pass filter to rows and the second letter refers to filter applied to columns. The elimination of high pass components by 2D wavelet transform technique reduces the computation time by reducing the number of arithmetic operations and memory accesses and communication energy by reducing the number of transmitted bits. With the increase in the levels of decomposition, the compression can be made efficient correspondingly, the inverse DWT are performed in the decompressor block. A Quantizer simply reduces the number of bits needed to store the transformed coefficients by reducing the precision of those values. Since this is a many to one mapping, it is a lossy process and is the main source of compression in an encoder. In uniform quantization, quantization is performed on each individual coefficient. Among the various coding algorithms, the embedded zero tree wavelet coding by have Shapiro and its improved version, the SPIHT by Said and Pearlman [6] been very successful. EZW is a progressive image compression © 2009 ACADEMY PUBLISHER