IMPLEMENTATION METHOD ON MEDICAL IMAGE COMPRESSION SYSTEM: A REVIEW <s> HARDWARE-BASED IMPLEMENTATION OF THE COMPRESSION SYSTEMS <s> In this paper we present FPGA implementation of Artificial Neural Networks for image compression. Image compression is a process which minimizes the size of an image file to an unacceptable level without degrading the quality of the image. The main components of an artificial neuron are adders and multipliers. In order to implement neural network, large number of adders and multipliers are required. The main constraint in the implementation of neural network is the area occupied by the multipliers. In order to overcome this area we propose the use of constant coefficient multiplier like distributed arithmetic (DA) based multiplier. The area efficiency is obtained by use of adders and shifters in Distributed arithmetic based multiplier architecture. The distributed arithmetic based multiplier is made use to implement 4∶4∶2∶2∶4 and 16∶16∶8∶8∶16 artificial neural network architecture for image compression. These architectures are implemented on FPGA and area efficiency is obtained. <s> BIB001 </s> IMPLEMENTATION METHOD ON MEDICAL IMAGE COMPRESSION SYSTEM: A REVIEW <s> HARDWARE-BASED IMPLEMENTATION OF THE COMPRESSION SYSTEMS <s> Medical image compression is necessary to store the huge database in Medical Centres and medical data transfer for the purpose of diagnosis. This paper attempts to improve the higher peak signal to noise ratio (PSNR) by using the system error compensation (SEC) method. The SEC design is based on quantization and curvelet transform (CT) decomposes the system error (E) to six scales. Only the scale 1 and scale 6 are constructed to error compensation. The simulation results are shown that the proposed method can improve 40.48 %, 9.69 % both in terms of bit rate and PSNR when compared to the conventional method. <s> BIB002
A key aspect of the image compression was the implementation of hardware-based. Architecture or algorithm was implemented in selected board depend on the applications. The performances of their architecture in the hardware were evaluated. Another issue regarding medical image compression was presented by BIB002 . A curveletbased medical image compression using system error compensation (SEC) was proposed. The quantisation and curvelet transform was used as the SEC algorithm. Output image and the system error can be combined to create output images, as demonstrated in Figure 6 . BIB002 Additionally, the curvelet transform was used to improve the PSNR while the encoding process was used to produce the image with high PSNR at low bit rate. This experiment was conducted using Intel (R) Core(TM) i5 CPU M520 @ 2.40GHz. In order to test this algorithm, four (4) standard images the size of 512*512 pixels and block size of 4*4 pixels (Airplane, Girl, Lena, and Pepper) and three (3) medical images (MRI cardiac tract, MRI skull, and Ultrasound Liver Cyst) have been used. The experimental results have shown that the proposed method can improve both the bit rate and PSNR by 40.48% and 9.69% respectively, when compared to conventional method. The main advantage of distributed arithmetic (DA) is its efficiency of mechanization, which is in contrast with the multiply-accumulate (MAC) structure. The MAC computes bits of a single input vector in parallel, while the DA serially computes individual bits of multiple inputs. Figure 7 shows the basic block diagram of the DA. The basic idea of the DA is to replace all multiplications and additions by applying a look-up-table (LUT) and shifter accumulator. In addition, this technique relies on the fact that one of the input coefficients is constant BIB001 . Which important difference and a prerequisite for DA techniques.