title: [Content-based automatic retinal image recognition and retrieval system].
abstract: This paper is aimed to fulfill a prototype system used to classify and retrieve retinal image automatically. With the content-based image retrieval (CBIR) technology, a method to represent the retinal characteristics mixing the fundus image color (gray) histogram with bright, dark region features and other local comprehensive information was proposed. The method uses kernel principal component analysis (KPCA) to further extract nonlinear features and dimensionality reduced. It also puts forward a measurement method using support vector machine (SVM) on KPCA weighted distance in similarity measure aspect. Testing 300 samples with this prototype system randomly, we obtained the total image number of wrong retrieved 32, and the retrieval rate 89.33%. It showed that the identification rate of the system for retinal image was high.
authors.affiliation: Information Center, Ningxia People's Hospital, Yinchuan 750021, China. zhangjm0415@126.com
authors.forename: Jiumei
authors.initials: J
authors.lastname: Zhang
authors.forename: Jianjun
authors.initials: J
authors.lastname: Du
authors.forename: Xia
authors.initials: X
authors.lastname: Cheng
authors.forename: Hongliang
authors.initials: H
authors.lastname: Cao
mesh_terms: D000465:Algorithms; D005654:Fundus Oculi; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D016247:Information Storage and Retrieval / Q000379:methods*; D009716:Numerical Analysis, Computer-Assisted; D009887:Ophthalmoscopy / Q000592:standards*; D010363:Pattern Recognition, Automated / Q000379:methods*; D012160:Retina / Q000473:pathology*; D012171:Retinal Vessels / Q000473:pathology