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1. AUTOMATED VESSEL SEGMENTATION USING INFINITE PERIMETER ACTIVE
CONTOUR MODEL WITH HYBRID REGION INFORMATION WITH APPLICATION
TO RETINAL IMAGES
ABSTRACT
Automated detection of blood vessel structures isbecoming of crucial interest for better
management of vasculardisease. In this paper, we propose a new infinite active contourmodel
that uses hybrid region information of the image toapproach this problem. More specifically, an
infinite perimeter regularize, provided by using L2 Lévesque measure of the-neighborhood of
boundaries, allows for better detection ofsmall oscillatory (branching) structures than the
traditionalmodels based on the length of a feature’s boundaries (i.e. H1Hausdorff measure).
Moreover, for better general segmentationperformance, the proposed model takes the advantage
of usingdifferent types of region information, such as the combination ofintensity information
and local phase based enhancement map.The local phase based enhancement map is used for its
superiorityin preserving vessel edges while the given image intensityinformation will guarantee a
correct feature’s segmentation. Weevaluate the performance of the proposed model by applyingit
to three public retinal image datasets (two datasets of colorfunds photography and one
fluorescein angiography dataset).The proposed model outperforms its competitors when
comparedwith other widely used unsupervised and supervised methods. Forexample, the
sensitivity (0:742), specificity (0:982) and accuracy(0:954) achieved on the DRIVE dataset are
very close to thoseof the second observer’s annotations.