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Region based Image Compression based on Textural Properties in MRI Images for Telemedicine Applications

G. Vallathan, Dr.K. Jayanthi

Abstract


The paper proposes a hybrid image compression model for efficient transmission of medical image using lossless and lossy coding for telemedicine application. Since storage space demands in hospitals are continually increasing compression of the recorded medical images is the need of the hour. In medical images only a small part should be diagnosis. Hence Region based coding (RBC) technique is substantial for medical image compression. Since lossless & lossy compression is significant in order to achieve high compression. But the very high compression ratio refers to the quality of the image. Given a particular compression ratio, the quality of the image reconstructed using the Huffman coding for compression would be better. This paper presents a method of employing both methods Integer wavelet transform (IWT) and the SPIHT (set partitioning in hierarchical trees) algorithm helps to reconstruct the medical image up to the desired quality. Finally the performance of method will be evaluated using the parameters like Peak Signal to Noise Ratio (PSNR), Mean Square Error (MSE), Quality factor and Compression ratio.

Keywords


Image Compression, Textural Properties, Telemedicine Applications

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References


V.K. Bairagi, A.M. Sapkal, Automated region based hybrid compression in digital imaging and communication in MRI images in telemedicine applications.

Miaou, S.-G., Ke, F.-S., Chen, S.-C.: ‘A lossless compression method for medical image sequences using JPEG-LS and interframe coding’, IEEE Trans. Inf. Technol. Biomed., 2009, 13, (5), pp. 818–821

Placidi, G.: ‘Adaptive compression algorithm from projections: application on medical grayscale images’, J. Comput. Biol. Med., 2009, 39, pp. 993–999

Baeza, I., Verdoy, A.: ‘ROI-based procedures for progressive transmission of digital images: a comparison’, J. Math. Comput. Model., 2009, 50, pp. 849–859

Javid Ali, T., Akhtar, P.: ‘Significance of region of interest applied on MRI and CT images in teleradiology-telemedicine’ (Springer-Verlag, Berlin, Heidelberg, 2008), pp. 151–159

Sanchez, V., Abugharbieh, R., Nasiopoulos, P.: ‘Symmetry-based scalable lossless compression of 3D medical image data’, IEEE Trans. Med. Imaging, 2009, 28, (7), pp. 1062–1071

Maglogiannis, I., Kormentzas, G.: ‘Wavelet-based compression with ROI coding support for mobile access to DICOM images over heterogeneous radio networks’, Trans. Inf. Technol. Biomed., 2009, 13, (4), pp. 458–466

Doukas, C., Maglogiannis, I.: ‘Region of interest coding techniques for medical image compression’, IEEE Eng. Med. Biol. Mag., 2007, 26, (5), pp. 29–35

Zhang, Q., Xiaio, X.: ‘Extracting regions of interest in biomedical images’. Int. Seminar on Future BioMedical Information Engineering, 2008, pp. 3–6

Yu, Y., Wang, B.: ‘Saliency based compressive sampling for image signals’, IEEE Signal Process. Lett., 2010, 17, (11), pp. 973–976

Calderbank, A.R., Daubechies, I., Sweldens, W., Yeo, B.L.: ‘Wavelettransforms that map integers to integers’, Appl. Comput. Harmon. Anal., 1998, 5, pp. 332–369

Sayood, K.: ‘Introduction to data compression’ (Elsevier Publication, 2010, 3rd edn.), pp. 473–512

Bairagi, V.K., Sapkal, A.M.: ‘Selection of wavelets for medical image compression’. IEEE Int. Conf. ACT 2009, IEEE Digital Library, Trivandrum, India, December 2009, DOI 10.1109/ACT.2009.172, pp. 678–680

Solomon, D., Motta, G.: ‘Handbook of data compression’ (Springer, 2010, 5th edn.), pp. 1–51[15] Black, P.E.: ‘Big-O notation’, in Black, P.E. (Ed.): tstructures [online]’ (U.S. National Institute of Standards and Technology, 2008)


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