IMPLEMENTATION METHOD ON MEDICAL IMAGE COMPRESSION SYSTEM: A REVIEW <s> 2. <s> This paper presents the design and implementation of three dimensional (3D) Haar wavelet transform (HWT) with transpose based computation and dynamic partial reconfiguration (DPR) mechanism on field programmable gate array (FPGA). Due to the separability property of the multi-dimensional HWT, the proposed architecture has been implemented using a cascade of three N-point one dimensional (1D) HWT and two transpose memories for a 3D volume of NxNxN suitable for real-time 3D medical imaging applications. These applications require continuous hardware servicing, hence DPR has been introduced. Two architectures were synthesised using VHDL and implemented on Xilinx Virtex-5 FPGAs. Experimental results and comparisons between different configurations using partial and non-partial reconfiguration processes and a detailed performance analysis of the area, power consumption and maximum frequency are analysed in this paper. <s> BIB001 </s> IMPLEMENTATION METHOD ON MEDICAL IMAGE COMPRESSION SYSTEM: A REVIEW <s> 2. <s> Telemedicine characterized by transmission of medical data and images between users is one of the emerging fields in medicine. Huge bandwidth is necessary for transmitting medical images over the internet. Resolution factor and number of images per diagnosis makes even the size of the images that belongs to a single patient to be very large in size. So there is an immense need for efficient compression techniques for use in compressing these medical images to decrease the storage space and efficiency of transfer the images over network for access to electronic patient records. This project summarizes the different transformation methods used in compression as compression is one of the techniques that reduces the amount of data needed for storage or transmission of information. This paper outlines the comparison of transformation methods such as DPCM (Differential Pulse Code Modulation), and prediction improved DPCM transformation step of compression and introduced a transformation which is efficient in both entropy reduction and computational complexity. A new method is then achieved by improving the perdiction model which is used in lossless JPEG. The prediction improved transformation increases the energy compaction of prediction model and as a result reduces entropy value of transformed image. After transforming the image Huffman encoding used to compress the image. As a result, the new algorithm shows a better efficiency for lossless compression of medical images, especially for online applications. The result is analyzed using MATLAB and implemented in hardware. <s> BIB002
Image compression is a well-established research area. However, medical image compression researchers, especially dealing with 3-D modalities, are still considered as in the infancy stage. Although newer compression methods algorithm optimizations have been proposed, a very limited hardware implementation of 3-D medical image compression is discussed BIB002 , BIB001 . The rest of the paper is organized as follows. An overview of the classification of compression method is presented in Section 2. Section 3 explains the hardware-based implementation of the compression systems and architecture. Software and hardwarebased implementation of the compression systems are described in Section 4. Finally, concluding remarks are given in Section 5.