Abstract
Problem statement: Nowadays a large number of various medical images are generated from hospitals and medical centers with sophisticated image acquisition devices, the movement toward digital images in radiology presents the problem of how to conveniently and economically store, retrieve and transmit the volume of digital images. Thus digital image data compression is necessary in order to solve this problem. So in a wide range of medical applications such as disease diagnostic and during the compression process, the loss of information is unacceptable; hence medical images are required to be at high resolution as possible. Instead of lossy compression with relatively high compression ratio, mathematical lossless compression methods are favored in this field. Approach: In this study, an efficient new lossless image coding algorithm using a simple technique was presented. Our coding algorithm was based on pixel redundancy reduction by formulating two matrices only, which were Gray Scale Matrix (GSM) and Binary Matrix (BM). These matrices had been used for coding and decoding processes. Results: Results showed that the maximum compression ratio achieved using the proposed method was 4:1, which was more efficient than the present lossless techniques, moreover the computational complexity is greatly simplified; therefore producing very fast coding and decoding. Conclusion: This algorithm was most suitable for those images where lossy compression was avoided such as medical images used for teleradiology and other telemedicine purposed and it can be applied to other medical modalities.
Highlights
Medical imaging is a powerful and useful tool for radiologists and consultants, allowing them to improve and facilitate their diagnosis
Lossless compression techniques do not permit any loss of information and allow the original signal to be recovered exactly
Rather than lossy compression with relatively high compression ratio, mathematical lossless compression methods are favored in this field[5]
Summary
Medical imaging is a powerful and useful tool for radiologists and consultants, allowing them to improve and facilitate their diagnosis. Image compression is a key factor to improve transmission speed and storage, but it risks losing relevant medical information[3]. It exploits common characteristics of most images that are the neighboring picture elements or pixels are highly correlated[4]. In applications dealing with speech signals and video television images, where some loss of information can be tolerated, lossy compression methods can be used. In this study we present a new coding algorithm for medical images This algorithm is absolutely lossless and based on pixel redundancy reduction using only two matrices for coding and decoding processes without affecting the quality of the resultant reconstructed image
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