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A Segmentation based Retrieval of Medical MRI Images in Telemedicine

机译:基于分段的远程医疗医学MRI图像检索

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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.
机译:远程医疗通过使用电信技术促进与位于偏远地区的医疗保健提供者的咨询。信息和通信技术是远程医疗中为患者提供临床服务的基础。远程医疗过程中的重要组成部分是医学图像的传输,以诊断疾病。在农村地区,医学图像的大尺寸加上带宽的限制是需要解决的挑战。基于内容的图像检索技术用于从数据库检索相关图像。它已成功实现用于医学图像检索。本文研究了利用压缩图像来有效利用带宽的远程医疗医学图像检索问题。提出了一种新颖的特征提取和遗传优化的神经网络分类器。实验研究表明,与未压缩图像相比,压缩图像的分类精度更高。扩散加权图像大脑的DWI图像用于测试提出的分类器检索受中风疾病影响的医学图像的效率。

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