Image Mining in IRIS: Integrated Retinal Information System Wynne Hsu, Mong-Li Lee, and Kheng Guan Goh   View Demo (HTML)   Return to Demonstrations Abstract There is an increasing demand for systems that can automatically analyze images and extract semantically meaningful information. IRIS, an Integrated Retinal Information system, has been developed to provide medical professionals easy and unified access to the screening, trend and progression of diabetic-related eye diseases in a diabetic patient database. This paper shows how mining techniques can be used to accurately extract features in the retinal images. In particular, we apply a classification approach to determine the conditions for tortuousity in retinal blood vessels. References Note: References link to DBLP on the Web. [1] Bing Liu , Wynne Hsu , Yiming Ma : Integrating Classification and Association Rule Mining. KDD 1998 : 80-86 BIBTEX @inproceedings{DBLP:conf/sigmod/HsuLG00,   author    = {Wynne Hsu and                 Mong-Li Lee and                 Kheng Guan Goh},    editor    = {Weidong Chen and                 Jeffrey F. Naughton and                 Philip A. Bernstein},    title     = {Image Mining in IRIS: Integrated Retinal Information System},    booktitle = {Proceedings of the 2000 ACM SIGMOD International Conference on                 Management of Data, May 16-18, 2000, Dallas, Texas, USA},    journal   = {SIGMOD Record},    publisher = {ACM},    volume    = {29},    number    = {2},    year      = {2000},    isbn      = {1-58113-218-2},    pages     = {593},    crossref  = {DBLP:conf/sigmod/2000},    bibsource = {DBLP, http://dblp.uni-trier.de} } }, DiSC'01 Copyright ©2002 ACM Inc.