Region based Coding of 3D Magnetic Resonance Images for Telemedicine Applications

Abstract

ABSTRACT Region Based Coding (RBC) technique is significant for medical image compression and transmission. Lossless compression schemes with secure transmission play a key role in telemedicine applications that help in accurate diagnosis and research. In this paper we propose a lossless compression approach based on 3D integer wavelet transform, 3D SPIHT algorithm of MR images. The use of lifting scheme allows to generate truly lossless integer to integer wavelet transforms. The main objective of this work is rejects the noisy background and reconstructs the image portion losslessly. In this work different integer wavelet transforms will be used to compress the 3D MR images. The performance of the system has been evaluated based on bits-per-pixel and peak signal-to-noise ratio. Keywords 3D SPIHT algorithm, Integer Wavelet Transform, Lossless compression, Medical Image Compression, Region Based Coding. 1. INTRODUCTION Image compression is useful reducing the storage and transmission bandwidth requirements of medical imges.Compression methods are classified into lossless and lossy methods. In the medical image scenario, lossy compression schemes are not generally used. This is due to possible loss of useful clinical information which may influence diagnosis. In addition to these reasons, there can be legal issues. Storage of medical images is generally problematic because of the requirement to preserve the best possible image quality which is usually interpreted as a need for lossless compression. 3D magnetic resonance image (MRI) data, which contains multiple slices representing a part of a body, requires compression for efficient storage and transmission. Compression of medical data is also required in telemedicine applications where image data needs to be transmitted over the network. Lossless compression, Progressive transmission and region of interest (ROI) are important functionalities for a compression scheme. Wavelet-based techniques are the latest development in the field of image compression. It offers multiresolution capability that is not available in any of the other methods. The wavelet transform analyzes a signal in time and scale. The low frequency components in the signal that are spread out in time, as well as the high frequency components that are localized in time are captured by a variable-length window [3]. The window is shifted by different units of time in a discrete manner, thus covering the entire signal. Lifting is a technique used in constructing second generation wavelets, entirely in the spatial domain [6]. The first generation wavelets are translates and dilates of a single mother wavelet, and Fourier techniques are essential in their construction. The lifting scheme does not use the Fourier technique [5]. It is a very fast method compared to the first generation wavelets. Moreover, the inverse transform is obtained by replacing addition with subtraction and reversing the operations in the forward transform. The goal of quantization is to encode the data from a source, with some loss, so that the best reproduction is obtained. Set partitioning hierarchical trees (SPIHT) achieves more compression than EZW [4].A typical MR image foreground contains clinical information which needs to be compressed without any loss. On the other hand the background does not contain any clinical information. It is only noise and consumes unnecessary bit budget and impairs the performance of a compression scheme. In this work we ignore the background part. We generate image masks in such a way that the foreground part is totally included and the pixel values in the foreground part are made zero. Morphological operations can be effectively used to generate image masks, which contain a value of „1‟ in the foreground and a value of „0‟ in the background. The original image is then multiplied with these masks to obtain “background noise free” images while keeping the information in the foreground part. In this paper we compare proposed work using different integer wavelets with the original scheme using 3DSPIHT algorithm.