Advances in telemedicine technology enable rapid medical diagnoses with visualization and quantitative assessment by medical practitioners.In healthcare and hospital networks,medical data exchange-based wireless local area network (WLAN) transceivers remain challenging because
of their growing data size,real-time contact with compressed images,and range of bandwidths requiring transmission support.Prior to transmission,medical data are compressed to minimize transmission bandwidth and save transmitting power.Researchers address many challenges in improving performance of compression approaches.Such challenges include energy compaction,
computational complexity,high entropy value,drive low compression ratio (CR) and high computational complexity in real-time implementation.Thus,a new approach called Enhanced Independent Component Analysis (EICA) for medical image compression has been developed to boost compression techniques;which transform the image data by block-based Independent Component Analysis (ICA).The proposed method uses Fast Independent Component Analysis
(FastICA) algorithm followed by developed quantization architecture based zero quantized coefficients percentage (ZQCP) prediction model using artificial neural network. For image reconstruction,decoding steps based the developed quantization architecture are examined.The EICA is particularly useful where the size of the transmitted data needs to be reduced to minimize the image transmission time.For data compression with suitable and effective performance,enhanced independent components analysis (EICA) is proposed as an algorithm for compression and decompression of medical data.A comparative analysis is performed based on existing data
compression techniques:discrete cosine transform (DCT), set partitioning in hierarchical trees (SPIHT),and Joint Photographic Experts Group (JPEG 2000).Three main modules,namely,compression segment (CS),transceiver segment (TRS),and outcome segment (OTS) modules,are developed to realize a fully computerized simulation tool for medical data compression with suitable and effective performance.To compress medical data using algorithms,CS module involves four different approaches which are DCT, SPIHT,JPEG 2000 and EICA.TRS module is processed by low-cost WLANs with low-bandwidth transmission.Finally,OTS is used for data decompression and visualization result.In terms of compression module,results show the benefits of applying EICA in medical data compression and transmission.While for system design,the developed system displays favorable outcomes in compressing and transmitting medical data.In conclusion,all three modules (CS,TRS,and OTS) are integrated to yield a computerized prototype named as Medical Data Simulation System(Medata-SIM) computerized system that includes medical data compression and transceiver for visualization to aid medical practitioners in carrying out rapid diagnoses
