iosr-JVSP   Volume-2 ~ Issue-4

ABSTRACT: With the development of CT, MRI, PET, EBCT, SMRI etc, the scanning rate and distinguishing rate of imaging equipment is enhanced greatly. Using wavelet technology, medical image can be processed in deep degree by denoising, enhancement, edge extraction etc, which can make good use of the image information and improve diagnosing. Compressions based on wavelet transform are the state-of-the-art compression technique used in medical image compression. For medical images it is critical to produce high compression performance while minimizing the amount of image data so the data can be stored economically. Modern radiology techniques provide crucial medical information for radiologists to diagnose diseases and determine appropriate treatments. Such information must be acquired through medical imaging (MI) processes. Since more and more medical images are in digital format, more economical and effective data compression technologies are required to minimize mass volume of digital image data produced in the hospitals. The wavelet-based compression scheme contains transformation, quantization, and lossless entropy coding. For the transformation stage, discrete wavelet transform and lifting schemes are introduced. In this paper an attempt has been made to analyse different wavelet techniques for image compression. Hand designed wavelets considered in this work are Haar wavelet, Daubechie wavelet, Biorthognal wavelet, Demeyer wavelet, Coiflet wavelet and Symlet wavelet. These wavelet transforms are used to compress the test images competitively by using Set Partitioning In Hierarchical Trees (SPIHT) algorithm. SPIHT is a new advanced algorithm based on wavelet transform which is gaining attention due to many potential commercial applications in the area of image compression. The SPIHT coder is also a highly refined version of the EZW algorithm.

Keywords – Wavelet, Medical Image, Compression, PSNR

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ABSTRACT:Due to its ease of implementation, the least mean square (LMS) algorithm is one of the most wellknown algorithms for mobile communication systems. However, the main limitation of this approach is its relatively slow convergence rate. In this paper two new variable step size Least Mean Square (LMS) adaptive filter algorithms are proposed. In the first algorithm two step sizes will be calculated from two values which will vary iteration to iteration. This algorithm is analogous to LMS algorithm, and produces better convergence performance compared to that of LMS. In the second algorithm also two step sizes are calculated based on a variable. This algorithm is analogous to Normalised Least Mean Square (NLMS) and produces better convergence performance compared to that of NLMS.

Keywords –LMS, NLMS, Binary Step, Channel Equalization.

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Binary Step Size Variations of LMS and NLMS
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ABSTRACT: In the recent growth of data intensive and multimedia based applications, efficient image compression solutions are becoming critical. The main objective of Image Compression is to reduce redundancy of the data and improve the efficiency. The main techniques used are Fourier Analysis, Discrete Cosine Transform vector quantization method, sub-band coding method. The drawbacks in the above methods are, they cannot be used for real time systems. In order to overcome these problems, the Wavelet Transform method has been introduced. Wavelet Analysis is highly capable of revealing aspects of data like trends, breakdown points, discontinuities in higher derivates and self similarity and can often compress or diagnose a signal without appreciable degradation. Here, we implement a lossy image compression technique using Matlab Wavelet Toolbox and Matlab Functions where the wavelet transform of the signal is performed, then calculated a threshold based on the compression ratio acquired by the user.

Keywords : CWT, DWT, Decomposition,, Haar Transform, Lossy Compression, Wavelet.

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