Objective: In medical image processing, storage and transmission of image pixels is a vital problem since diagnosis is a continuous process.
Methods: The 2D maximum entropy method segmentation followed by compression provides good results in the field of Medical image processing which has storage and transmission problems.The proposed algorithm is created using 2-D maximum entropy method segmentation and followed by Hybrid image compression.This proposal introduces an advanced compression method which combines both lossy and lossless image compression techniques. The clinical part of the image is segmented with 2-D maximum entropy thresholding and lossless techniques are applied to compress this part. Lossy image compression technology is implemented in other part of the image that is with the background picture. Run Length Encoding is applied to the resultant data to produce the compressed image.
Findings: The Advantage of using 2-D Maximum entropy method is that it considers both the grey information and neighbouring information. It can be able to produce good results even the image’s signal to noise ratio (SNR) is low. Among lossy and lossless image compression methods lossless method is preferred in medical image processing since it has to protect important clinical information. Lossless compression can be able to produce lower compression ratio than lossy because it reduces the size of image only to certain limit. The experimental result shows that the proposed method provides good compression ratio and PSNR measures.
Novelty/Improvements:Future enhancement of this algorithm is that the neural network algorithm such as self-organizing feature map can be used to find out the threshold value automatically.
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