Abstract: Medical image modalities such as computed
tomography (CT), magnetic resonance imaging (MRI), ultrasound
(US), X-ray are adapted to diagnose disease. These modalities
provide flexible means of reviewing anatomical cross-sections and
physiological state in different parts of the human body. The raw
medical images have a huge file size and need large storage
requirements. So it should be such a way to reduce the size of those
image files to be valid for telemedicine applications. Thus the image
compression is a key factor to reduce the bit rate for transmission or
storage while maintaining an acceptable reproduction quality, but it is
natural to rise the question of how much an image can be compressed
and still preserve sufficient information for a given clinical
application. Many techniques for achieving data compression have
been introduced. In this study, three different MRI modalities which
are Brain, Spine and Knee have been compressed and reconstructed
using wavelet transform. Subjective and objective evaluation has
been done to investigate the clinical information quality of the
compressed images. For the objective evaluation, the results show
that the PSNR which indicates the quality of the reconstructed image
is ranging from (21.95 dB to 30.80 dB, 27.25 dB to 35.75 dB, and
26.93 dB to 34.93 dB) for Brain, Spine, and Knee respectively. For
the subjective evaluation test, the results show that the compression
ratio of 40:1 was acceptable for brain image, whereas for spine and
knee images 50:1 was acceptable.
Abstract: Wavelet transforms are multiresolution
decompositions that can be used to analyze signals and images.
Image compression is one of major applications of wavelet
transforms in image processing. It is considered as one of the most
powerful methods that provides a high compression ratio. However,
its implementation is very time-consuming. At the other hand,
parallel computing technologies are an efficient method for image
compression using wavelets. In this paper, we propose a parallel
wavelet compression algorithm based on quadtrees. We implement
the algorithm using MatlabMPI (a parallel, message passing version
of Matlab), and compute its isoefficiency function, and show that it is
scalable. Our experimental results confirm the efficiency of the
algorithm also.
Abstract: In this paper we have proposed three and two
stage still gray scale image compressor based on BTC. In our
schemes, we have employed a combination of four techniques
to reduce the bit rate. They are quad tree segmentation, bit
plane omission, bit plane coding using 32 visual patterns and
interpolative bit plane coding. The experimental results show
that the proposed schemes achieve an average bit rate of 0.46
bits per pixel (bpp) for standard gray scale images with an
average PSNR value of 30.25, which is better than the results
from the exiting similar methods based on BTC.
Abstract: In this paper a hybrid technique of Genetic Algorithm
and Simulated Annealing (HGASA) is applied for Fractal Image
Compression (FIC). With the help of this hybrid evolutionary
algorithm effort is made to reduce the search complexity of matching
between range block and domain block. The concept of Simulated
Annealing (SA) is incorporated into Genetic Algorithm (GA) in order
to avoid pre-mature convergence of the strings. One of the image
compression techniques in the spatial domain is Fractal Image
Compression but the main drawback of FIC is that it involves more
computational time due to global search. In order to improve the
computational time along with acceptable quality of the decoded
image, HGASA technique has been proposed. Experimental results
show that the proposed HGASA is a better method than GA in terms
of PSNR for Fractal image Compression.