An Intelligent Information Management System for Retinal Image Storage and Recognition in Chronic Disease using Digital Signal and Image Processing

Xiaojun Xu, Zhong Tang, Xijia He, Haixuan Wang, Anil Sharma


Retinal imaging plays a very significant role in the study of retinal vasculature changes indicative of chronic disease information related to vision. This study investigates the invulnerability of retinal image information in the disease information system and assesses the quantitative method of the morphological changes in the retinal vascular network. In this work, the medical digital image transmission protocol Digital Imaging & Communications in Medicine (DICOM) version 3.0 and the retinal image Picture Archiving & Communication System (PACS) were constructed in the laboratory using browser/server mode. Also, the DICOM-SR document was designed in this article using a list or hierarchy, and the retinal images to report the information of patients by using the Hypertext Transfer Protocol (HTTP) – based Web Access to DICOM Persistent Objects (WADO) approach. The results showed that the retinal image PACS system constructed in Browser/Server mode can effectively store and transmit DICOM images. When the imaging device is combined with the application program, special adapters are used to negotiate the transmission syntax. The message flow is decoded in the communication process, which can be connected with the realization to improve the efficiency of information collection. The proposed PACS system integrates the quantitative features of retina providing more meaningful research data for data mining in comparison to the traditional state of the art methods based on chronic disease management system. The diagnostic ability of the retinal imaging procedure using the DICOM images is justified by obtaining 98.51%, 98.04%, 99% and 99.01% of accuracy, sensitivity, specificity and precision values respectively.


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