科研之友
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摘要
Telemedicine facilitates consultation with the health care provider located in a remote location by using telecommunication technology. Information and communication technologies are the backbone in telemedicine to provide clinical services for patients. A vital component in the telemedicine process is the transfer of medical images in order to diagnose a disease. The large size of medical images compounded with bandwidth limitations in rural areas are challenges that need to be addressed. Content based image retrieval techniques are used to retrieve relevant images from the database. It has successfully been implemented for medical image retrieval. This paper investigates the medical image retrieval problem for telemedicine using compressed images for efficient utilization of bandwidth. A novel feature extraction and a genetic optimized neural network classifier were proposed in this study. Experimental studies revealed better classification accuracy for compressed images when compared to the uncompressed ones. Diffusion Weighted Images DWI images of the brain were used to test the efficiency of the proposed classifier to retrieve medical images affected with Stroke disease.
- 出版日期2013-2
