title: Automatic vessel network features quantification using local vessel pattern operator.
abstract: Automatic measurement and quantification of blood vessels' features and detection of vessel landmarks are key steps in the computer-aided diagnosis and diseases monitoring. This work proposes a novel and robust method for detecting vessel landmarks, i.e. bifurcation and crossovers, and measurement of different features, i.e. vessel orientation and vessel diameter as well as bifurcation angle, from the detected vessel network using simple and efficient local vessel pattern operator. The proposed method is applied to the publicly available DRIVE, STARE and ARIA databases and compared with existing state-of-the-art approaches. It shows higher accuracy in detection of vessel landmark and estimation of vessel features.
authors: Department of Computer Engineering, The University of Razi, Kermanshah, Iran. fathi@eng.ui.ac.ir
authors: Abdolhossein
authors: A
authors: Fathi
authors: Ahmad Reza
authors: AR
authors: Naghsh-Nilchi
authors: Fardin Abdali
authors: FA
authors: Mohammadi
mesh_terms: D000465:Algorithms; D016208:Databases, Factual; D003941:Diagnostic Techniques, Ophthalmological; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D012164:Retinal Diseases / Q000175:diagnosis / Q000473:pathology; D012171:Retinal Vessels / Q000033:anatomy & histology* / Q000473:pathology*