VOL. 13, NO. 5, MARCH 2018 ISSN 1819-6608 ARPN Journal of Engineering and Applied Sciences ©2006-2018 Asian Research Publishing Network (ARPN). All rights reserved. www.arpnjournals.com 1877 MEDICAL IMAGE DATA COMPRESSION USING HYBRID METHODS Alyaa H. Ali Department of Physics, College of Science, University of Baghdad, Baghdad, Iraq E-Mail: aliahusain@ymail.com ABSTRACT This search focuses on Image Data compression using different methods for data compression, three images are simulated for this technique based on modified method, Huffman with local and soft threshed and three block size 4×4, 8×8 and 16×16. The second one is based on using the DCT "discrete cosine transformation" and "discrete wavelet transformation" DWT these methods are applied on the stroke brain images" Computing tomography CT images " after using the region based segmentation method to get the region of interest which is the stroke, Completing the process by calculating quality of image compression, five parameter are used ," Peak Signal to Noise Ratio (PSNR)", "Mean Square Error (MSE)","Compression Ratio(CR)","Structural Similarity Index (SSIM)" and "Universeral Image quality Index (UIQI)". Keyword: DCT, DWT, Huffman coding, region based method. INTRODUCTION In medical image processing the need for the image compression is essentially important and can divided into two structure, the first one is the lossy image compression higher value of CR "compression Ratio is obtained but, the compressed image is Distorted comparing it to the original image while the lossless image compression is identical with the original one [1]. The need for the image compression require considerable storage capacity, since the medical images such as Computed Tomography CT, X-ray and Magnetic there typical size range are large and the challenge to limited band compression is one of the methods used to reduce the image size. Different methods are developed all these method tend to achieving high quality of decompressed image, the compression can be accomplished either by lossy or lossless methods [2]. The image compression technique is used to save the important data which used only in the analysis. The image compression can be divided into two phrase, the first one is known as the lossless, in this one no information is lost, the original image is completely the same as the compressed image. The most known one is the Huffman coding. The lossy one, the original image cannot match the compressed image, this compression techniques reach image compression by losing part of the information while keeping the reconstruction quality. Hence, the data cannot be restore exactly the same as the original image [3]. The most commune methods is the Discrete Cosine Transformation and Wavelet Transformation [4] the image compression decrease the size of image storage bite without losing the quality of the image, also the time required to store or send the image decrease. The edge and the pixels which repeated be decrease by image compression [5]. The DCT based on separating the compressed image into different frequency parts, it consider to be lossy because the first part of the compression called quantization and the other which is not important frequency are removed [6] so, the "DCT" basics idea depend on "disintegrating the image into segment" [7], the lossy data compression give best compression ratio[8]. The wavelet is early appear in 1990, its idea is based on dividing the information of the image into four sub-bands in which the image is transformed into "low image information" and the remaining details are in three images " Horizontal, Vertical, and Diagonal" images. The first one is also decomposed into other four sub- images, this process gives a number of features which cannot be seen in the original images and can appear in the level after the transformation so, for this the "wavelet transformation" is the best for medical image compression. The Huffman coding which is produced by Doctor David. A. Huffman in 1952 it is a tool to build the image with less redundancy coding, the Huffman coding is a statistical coding, it try to minimize the number of the bits which is needed to represent the symbols [3]. These method is applied in this search the region of interest which is obtained using the "Base Region Method" which is the selection of the region of interest to compressed in the medical images of the diagnostic region, this method is used to have balance between the very good quality of the reconstructed and low memory Douckas et al. in 2007 has used the "Volumetric medical images" to analysis different region of interest [9]. Test images: The images which are used in the search are three brain stroke Gray Images of size 255×255, JPG type, (a) shows the gray scale image without skull and some noise in the image, (b) shows the image after using morphological properties "Erosion and Dilation " to remove the outer skull, (c) gives the region of interest which is the stroke after using the "region based method"[10].