Performance Analysis of Medical Image Compression Techniques
A large amount of medical data is generated through advanced medical imaging modalities. The digitization of medical image information is of immense interest to the medical community to reduce transmission time, storage costs, and for implementation of th
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Abstract A large amount of medical data is generated through advanced medical imaging modalities. The digitization of medical image information is of immense interest to the medical community to reduce transmission time, storage costs, and for implementation of the e-healthcare system like telemedicine (Journal of Medical Imaging and Health Informatics 1:300–306, 2011) [1]. Digital images in their original state require considerable storage capacity and transmission bandwidth. In this paper, an exhaustive comparative analysis of different compression techniques and their applications in the emerging fields of medical science such as telemedicine and teleconsultation has been carried out. The performance of compression algorithm can be measured using objective measures such as MSE, PSNR, SSIM, and correlation. Keywords Image compression DWT SPIHT JPEG2000
Compression ratio
Telemedicine
DCT
1 Introduction With the rapid growth of medical services such as e-health, telemedicine, and teleconsultation it is required to develop fast and efficient medical image analysis and compression techniques. It is important to transmit medical images in a com-
N.R. Patel (&) Faculty of Engineering & Technology, C. U. Shah University, Wadhwancity, Gujarat, India e-mail: [email protected] A. Kothari E.C. Department, Atmiya Institute of Technology & Science, Rajkot, Gujarat, India e-mail: [email protected] © Springer Science+Business Media Singapore 2016 S.C. Satapathy et al. (eds.), Proceedings of International Conference on ICT for Sustainable Development, Advances in Intelligent Systems and Computing 408, DOI 10.1007/978-981-10-0129-1_54
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pressed and secure form so that efficient medical diagnosis can be performed by the medical practitioner. For correct diagnosis it is necessary that the compression method preserves all important data. Lossless image compression techniques help to compress the image as well as maintain relevant information, but compromise the compression ratio. Lossy compression techniques are more efficient in terms of compression ratio but have significant loss of image quality and cannot preserve the characteristics needed in medical image processing and diagnosis. To optimize the above requirements, i.e., high compression ratios and the preservance of relevant information, the ROI-based compression is one of the best options to achieve the optimum compression ratios without any loss of useful information; it is basically done by selecting different important regions of an image along with the background and then compression methodology is applied on these regions separately and not on the whole image. Low compression level is applied on the useful regions while high compression is applied on the unimportant regions and the background. As a result, very high CRs are achieved by this methodology without any appreciable loss of information and clarity of image [2, 3]. This paper, in continuation with this thought, analyzes the suitability of lossy techniqu
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