Medical Image Compression Using Wavelets
Kondreddi Gopi · IOSR Journal of VLSI and Signal processing · 2013
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.