Proposal image compression Simulation of Medical Data Compression and Transmission Through Wireless LANs Mustafa Almahdi Algaet1, Zul Azri Bin Muhamad Noh1, Abd Samad Bin Hasan Basari1 ,, Abdul Samad Shibghatullah1, Ali Ahmad Milad1 and Aouache Mustapha2 Department of Computer System and Communication, Faculty of Information and Communication Technology, Universiti Teknikal Malaysia Melaka, Hanngtuha Jaya,76100 Durian Tunggal, Melaka, Malaysia1 Department of Electrical, Electronic and System Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, Bangi 43600, Selangor, Malaysia2 Introduction Procedures Results (Continued) Telemedicine means, transfer of data or image for one place to another for diagnostic purpose i.e., any type of medical services at a distance. Telemedicine ensures delivery of right medical advice at the right place at the right time Telemedicine uses telecommunications to deliver health care, often over great distances, with the possibility of cost savings particularly in remote and rural areas Figure 3. Block diagram and overview of the proposed system Description of the design framework The design framework for the development of the proposed MIT system for medical data exchange and transmission involves three main phases that are: Compression Segment (CS) Transmission Segment (TS) Receiver Segment (RS) The CS comprises three different compression algorithms involving (1) Discrete Cosine Transform-DCT (2) Set Partitioning in Hierarchical Tress-SPIHT (3) the Proposed Compression Algorithm- PCA to compress the medical image data. The TS deals with implementation of WiFi transmission of compressed medical data to transfer data. The approaches involved in this stage are: (i) 802.11b simulation (ii) DBPSK Modulation and (iii) Noise and packet size variation. Finally, the RS for the receiving involves integration of Wifi receiver, to collect the medical data from the TS through Wifi, and the decompression to retrieve and measure the original data. The detail descriptions of the three main development segments are described in the subsequent sub-sections. Figure 7 shows the PSNR result on data transmission simulation using grayscale radiography image with various Es number. Based on the result, the lower Es number produces lower PSNR value Efficiency and Evaluation The efficacy of the image received process is evaluated by comparing features of the medical input data image to the received /decompressed image. When the features of the input data images are similar to that of the received, the images are labeled as relevant similar images and the performance of the system is extraordinary. In this work, for MIT performance evaluation, the matrix performance (MP) was used since evaluation was based on a given cut-off measure, We can estimate the performance by applying the following two essential criteria: the compression ratio (CR )and the quality measurement of the reconstructed image( PSNR) (a) Compression ratio   (b) Distortion measure (c) Peak Signal-to-Noise Ratio (PSNR) (d) Signal to noise ratio (e) Peak mean square error Demo of Functionality Figure 9 shows system demo to test and implement the image retrieval and classification platforms. Figure 8 shows the BER computation result on Wi-Fi transmission simulation using RGB natural image with various Es number in Mbps packet data. Objective To examine the performance existing technique of data compression implemented in transmission via hospital network To develop a new scheme for data compression that suitable for effective medical data transmission To develop a completely data transmission simulation system for possible used in medical environment that can facilitated practitioner in making data exchange Radiography Images Figure 2. Data transmission simulation using proposed image compression on grayscale radiography image with various Es Number: (a) 0 dB, (b) 2 dB, (c) 4 dB, (d) 6 dB, (e) 8 dB, (f) 10 dB, (g) 12 dB and (h) original image Figure 1. Wi-Fi transmission simulation using grayscale radiography image on various Es Number: (a) 0 dB, (b) 2 dB, (c) 4 dB, (d) 6 dB, (e) 8 dB, (f) 10 dB, (g) 12 dB and (h) 14 dB Proposal image compression Figure 4 shows the main phases in the MIT design framework. Results Figure 5 show the PSNR result on Wi-Fi transmission simulation using RGB natural image with various Es number. Based on the result, the lower Es number produces lower PSNR value. Figure 6 shows the BER computation result on Wi-Fi transmission simulation using RGB natural image with various Es number in Mbps packet data. Conclusion The necessity for valid, effective and practical image transmission system has become apparent as more healthcare professionals start to adopt this system in the course of their daily activities. Hence, the research field is rapidly expending even if building a project class system that is robust and reliable is very complex. Nevertheless, the benefit and profit that would be gained by the healthcare community are unimaginable. Therefore, I a new proposal system was successfully developed for medical data transmission/receiving using compression approaches with high robust in term of image quality endurance to noise caused by transmission signal and transmission based Wi-Fi IEEE 802.11b. The prototype system is developed for medical practitioner and is meant to allow them to transfer data from server to user. Thus, it will allow for an effective transmission which is significantly important in hospital network and medical data exchange. References Further information please contact. Mustafa Almahdi Algaet and Dr. Zul Azri Muhamad Noh Department of Computer System and Communication, Faculty of Information and Communication Technology, Universiti Teknikal Malaysia Melaka, (UTeM) Tel: : +60 176808719 , +606 331 6663 Email: malgaet@gmail.com , zulazri@utem.edu.my