Author Topic: Medical Image Compression Using Multiwavelets for Telemedicine Applications  (Read 2926 times)

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Author : R.Sumalatha, M.V.Subramanyam
International Journal of Scientific & Engineering Research Volume 2, Issue 9, September-2011
ISSN 2229-5518
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Abstractó In this paper we propose an efficient region of interest (ROI) coding technique based on multiwavelet transform, set partitioning in hierarchial (SPIHT) algorithm of medical images. This new method reduces the importance of background coefficients in the ROI code block without compromising algorithm complexity. By using this coding method the compressed bit stream are all embedded and suited for progressive transmission. Extensive experimental results show that the proposed algorithm gives better quality if images using multiwavelets compared to that of the scalar wavelets. The performance of the system has been evaluated based on bits per pixel (bpp) , peak signal to noise ratio (PSNR)and mean square error (MSE).
Index Termsó Medical image compression, Multiwavelet, Region of interest (ROI), SPIHT, Telemedicine, Scalar wavelets, PSNR, MSE. 

1   INTRODUCTION                                                                      
ANALYSIS and compression of medical imagery is an important area of biomedical engineering. Medical image analysis and data compression are rapidly evolving field with growing applications in the healthcare services e.g teleradiology, teleconsultation, e-health, telemedicine and statistical medical data analysis[1]. For the telemedicine, medical image compression and analysis may even be more useful and can play an important role for the diagnosis of more sophisticated and complicated images through consultation of experts[2]. In medical image compres-sion diagnosis and analysis are effective only when compression techniques preserve all the relavant and important image information needed. This is the case of lossless compression. On the otherhand lossy compression is more efficient interms of storage and transmission needs but there is no gauranty to preserve the characteristics needed in medical diagnosis [3]. To avoid the above problem, there may be third option that the diagnostically important region (ROI) of the image is lossless compressed. ROI, a segmentation approach can be used to extract the ROI. These regions are very useful for diagnosis.Hence, the ROI must be compressed by a lossless or a near lossless compression algorithm.
Wavelet based techniques are latest development in the field of medical image compression. In most cases, the wavelet transform produces floating point coefficients and although this allows perfect reconstruction of the original image in theory, the use of finite precision arithmetic together with quantization results in a lossy scheme.Another way to achieve improved compression
results over wavelets is to use integer wavelets. Intger wavelet transform demonstrate a significant improve-ment in reconstructed image quality over the wavelet transform for medical images[4]. For best performance in image compression, wavelet transforms require filters that combine a number of desirable properties, such as orthgonality and symmetry. However, the design possibilities for wavelets are limited because they cannot simultaneously possess all of the desirable properties. The relatively new field of multiwavelets shows promise in obviating some of the limitations of wavelts. Multiwavelets offer more design options and are able to combine several desirable transform features.
This paper is organized as follows: section two re-gion of interest section three describes wavelet and integer wavelet transforms, section four describes mul-tiwavelet, section five describes SPIHT algorithm, sec-tion six experimental results section seven describes conclusion and future work.
ROI coding is one of the most important features provided by JPEG-2000. It allows, imposing heteroge-neous fidelity constraints to different regions of the image rather than encoding the entire image as a single entity. This property is especially useful for image cod-ing applications, where the image consists of regions that can be encoded at different bit rates, such as com-pression of medical images [5]. For most medical im-ages, the diagnostically significant information is loca-lized over relatively small regions of interest. In this case, region-based coding for better utilization of the available bit rate since the high quality should be maintained only for the aforementioned diagnostically significant regions and the rest of the image can be en-coded at a lower bit rate. Once the region of interest is selected efficiently, the significant region is transformed using lossless integer multiwavelet transform filter. Then the transformed images are encoded using SPIHT algorithm.
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 low frequency components in the signal that are spread out in time, as well as the high fre-quency components that are localized in time are cap-tured by a variable-length window[6]. The window is shifted by different units of time in a discrete manner, thus covering the entire signal. The wavelet transform (WT), in general, produces floating point coefficients. Although these coefficients can be used to reconstruct an original image perfectly in theory, the use of finite precision arithmetic and quantization results in a lossy scheme. Recently reversible integer wavelet transforms have been introduced [7]. Lifting is a technique used in constructing second generation wavelets, entirely in the spatial domain. 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. 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.
Multiwavelets are defined using several wavelets with several scaling functions. Multiwavelets have several advantages in comparision with scalar wavelet. The features such as compact support, orthogonality, symmetry, and higher order approximation are known to be mportant in signal processing. Multiwavelets provide one alternative to the wavelet transform. Multiwavelets are very similar to wavelets but have some important differences. In particular, where as wavelets but have an associated scaling function and wavelet function, multiwavelets have two or more scaling and wavelet function. Multifilter construction methods are already being developed to exploit the useful properties such as ortogonality, symmetry and high order of approximation [8]. However, the multi-channel nature of mutiwavelets also means that the sub band structure resulting from passing a signal through a multifilter bank is different. A single level of standard wavelet decomposition splits the input signal into lowpass and highpass coefficients through filtering and down sampling. A multi-level wavelet filter bank involves iterating the low pass - high pass filtering and down sampling procedure only on the output of the lowpass branch of the previous stage. During a single level of decomposition using a scalar wavelet transform, the 2D image data is replaced with four blocks corresponding to the sub bands. The multiwavelets used here have two channels, so there will be two sets of scaling coefficients and two sets of wavelet coefficients since multiple iterations over the lowpass data are desired, the scaling coefficients for the two channels are stored together. Likewise, the wavelet coefficients for the two channels are also stored together.

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