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1 Introduction Along with the development in medical radiological technology, medical images have become the most important means in clinical diagnosis and treatment. However, almost all kinds of digital medical images, especially those obtained by magnetic resonance (MR) imaging, are poorly illuminated [24]. Generally, they have characteristics such as noise, blurry edges, low contrast, and even image artifacts. These characteristics pose great difficulty for follow-up analysis and affect the accuracy of the doctor’s diagnosis to a certain degree. In many cases

. M., & Olshen, R. A. (1994). Evaluating quality of compressed medical images: SNR, subjective rating, and diagnostic accuracy. Proceedings of the IEEE , 82 (6), 919–932. Detyna, J., Jeleń, L., & Jeleń, M. (2011). Role of Image Processing in the Cancer Diagnosis. Bio-Algorithms and Med-Systems , 7 (4), 5–9. European Society of Radiology (ESR). (2011). Usability of irreversible image compression in radiological imaging. A position paper by the European Society of Radiology (ESR). George, A., & Livingston, S. J. (2013). A survey on full reference image quality

, 85–96 (2000). [5] [6] A. Przelaskowski, “Vector quality measure of lossy compressed medical image”, Comput. Biol. Med. 34, 193–207 (2004). [7] Jasper: [8] JJ2000: [9] Kakadu: [10] M.D. Adams and F. Kossenini, “JasPer: a software-based JPEG-2000 codec implementation”, in Proc. IEEE Int. Conf. Image Process

1 Introduction In clinical diagnosis, medical imaging plays a crucial role which helps in understanding and analysis of the organs. Medical imaging is a technique to create an internal image of a human body for clinical or medical purpose. Medical images are obtained by using different modalities that includes X-ray, computed tomography (CT), positron emission tomography and magnetic resonance imaging (MRI). Segmentation is an indispensable step in medical image analysis which helps in identifying appropriate therapy for abnormal changes in tissues and organs. In

References 1. Bhonsle, D., V. Chandra, G. R. Sinha. Medical Image Denoising Using Bilateral Filter. I. J. Image. – Graphics and Signal Processing, Vol. 4 , 2012, No 6. pp. 36-43. DOI:10.5815/ijigsp.2012.06.06. 2. Gonzalez, R. C., R. E. Woods. Digital Image Processing. Second Ed. Prentice-Hall, Inc., 2002. 3. Mallat, S. G. A Theory for Multiresolution Signal Decomposition: The Wavelet Representation. – IEEE Transaction on Pattern Recognition and Machine Intelligence, Vol. 11 , 1987, No 7. pp. 674-695. DOI:10.1109/34.192463. 4. Donoho, D. L., I. M. Johnstone

). Design space exploration of parallel embedded architectures for native Clifford algebra operations, IEEE Design and Test of Computers 29 (3): 60–69. Franchini, S., Gentile, A., Sorbello, F., Vassallo, G. and Vitabile, S. (2013). Design and implementation of an embedded coprocessor with native support for 5D, quadruple-based Clifford algebra, IEEE Transactions on Computers 62 (12): 2366–2381. Franchini, S., Gentile, A., Sorbello, F., Vassallo, G. and Vitabile, S. (2015). ConformalALU: A conformal geometric algebra coprocessor for medical image processing, IEEE

1 Introduction Medical imaging plays a vital role in health informatics, as it aids in diagnosis and decision-making. Tremendous progress in information and communication technologies has enabled easier access, modification, and distribution of digital data. New challenges, especially those related to security, arise when these evolving techniques are applied to healthcare systems. Sensitive data related to a patient demands further measures such as confidentiality and data integrity to ensure that the trustworthiness of the data is asserted. During communication

N. Krishna Santosh, Soubhagya Sankar Barpanda 4 Wavelet applications in medical image processing Abstract: Over the past 10 years, there is a rapid evolution in modalities of biomedi- cal imaging. They act as aid for doctors in disease identification, estimation of dis- ease occurrence and methodological treatment, thus degree of patient care has greatly strengthened. Medical images are usually generated either by ionizing radi- ation such as X-rays and gamma rays or by nonionizing radiation such as ultra- sound and magnetic resonance imaging (MRI) techniques

DOI 10.1515/bams-2013-0002      Bio-Algorithms and Med-Systems 2013; 9(1): 9–16 Wioletta W ó jtowicz* An introduction to watermarking of medical images Abstract: This paper provides a preliminary investigation on digital watermarking as an effective technology to pro- tect property rights and limit distribution of multimedia data. First, crucial properties and design requirements of watermarking schemes are discussed. Then, as water- marking techniques finds many applications in health- care industry, aspects of medical image watermarking are raised

COMPUTATIONAL METHODS IN APPLIED MATHEMATICS, Vol. 10 (2010), No. 3, pp. 235–274 c© 2010 Institute of Mathematics of the National Academy of Sciences of Belarus DIFFEOMORPHIC MATCHING AND DYNAMIC DEFORMABLE SURFACES IN 3D MEDICAL IMAGING R.AZENCOTT1, R.GLOWINSKI2, J. HE2, A. JAJOO2, Y. LI2, A.MARTYNENKO2, R.H.W.HOPPE3, S. BENZEKRY4, S.H. LITTLE5, AND W.A. ZOGHBI5 Abstract — We consider optimal matching of submanifolds such as curves and surfaces by a variational approach based on Hilbert spaces of diffeomorphic transfor- mations. In an abstract setting, the