Authors: M. Moorthi; R. Amutha
Addresses: Department of Electronics and Communication Engineering, Sri Chandrasekharendra Saraswathi Viswa Mahavidyalaya University, Kanchipuram, PITAM, Chennai, India ' Department of Electronics and Communication Engineering, SSN College of Engineering, Chennai, India
Abstract: Telemedicine network is used to transmit medical images from hospital to remote medical centres for diagnosis. In this connection, progressive quality coding algorithm has been developed to save storage space and better utilisation of bandwidth and to improve speed of data transmission. Generally, lossless compression should be used for region of interest (ROI) and lossy compression should be used for region of background (ROB) with a lower quality. In existing system, ROI is selected manually, but ROI is selected automatically in the proposed method, pre-processing is done to improve the visual quality of the image. Segmentation is carried out accurately and efficiently using canny edge operator and morphological processing method. The classification is done in medical image using particle swarm optimisation. ROB part of an image is compressed using set partition in hierarchical tree (SPIHT) algorithm in near lossless manner. Finally, the ROI is superimposed in compressed non-ROI (ROB) image. This method improves the compression ratio and increases the PSNR value compared to existing method. The proposed method is used for implementations of teleradiology and digital picture archiving and communications (PACS) systems practically.
Keywords: image compression; image segmentation; particle swarm optimisation; PSO; image decompression; compression ratio; progressive quality coding; medical images; telemedicine; image quality; SPIHT; PSNR value; peak SNR; signal to noise ratio; teleradiology; picture archiving and communications; digital PACS.
International Journal of Telemedicine and Clinical Practices, 2015 Vol.1 No.2, pp.125 - 140
Available online: 09 Jun 2015 *Full-text access for editors Access for subscribers Purchase this article Comment on this article