title: An improved gradient vector flow algorithm for optic disc segmentation.
abstract: The optic disc provides important cues for accurate diagnosis of various retinopathic diseases. Accurate segmentation of the optic disc is therefore an important step in the analysis of retinal images. Gradient vector flow (GVF) based segmentation algorithms have been used successfully on a variety of medical imagery, however, due to the compromise of internal and external energy forces, it can lead to less accurate segmentation in certain cases. In this paper, we show, that through incorporation of a mean shift term into the GVF framework, improved segmentation accuracy can be achieved. Experimental results on a large dataset of retinal images demonstrate that the presented method reliably detects the border of the optic disc.
authors.affiliation: The Institute of Electronics, Communications and Information Technology (ECIT), Queen's University Belfast, United Kingdom.
authors.forename: Huiyu
authors.initials: H
authors.lastname: Zhou
authors.forename: Gerald
authors.initials: G
authors.lastname: Schaefer
authors.forename: Tangwei
authors.initials: T
authors.lastname: Liu
authors.forename: Faquan
authors.initials: F
authors.lastname: Lin
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D003930:Diabetic Retinopathy / Q000473:pathology*; D005260:Female; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008297:Male; D008875:Middle Aged; D009898:Optic Disk / Q000473:pathology*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D042262:Retinoscopy / Q000379:methods*; D012680:Sensitivity and Specificity; D013382:Subtraction Technique