Blood Vessel Segmentation - A Supervised Method

14 Ott 2014
A Supervised Method by Using Gray-Level and Moment Invariants-Based Features
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A Supervised Method by Using Gray-Level and Moment Invariants-Based Features

Blood vessel segmentation, by Migliorati Andrea

Abstract—This paper presents a new supervised method for blood vessel detection in digital retinal images. This method uses a neural network (NN) scheme for pixel classification and computes a 7-D vector composed of gray-level and moment invariants-based features for pixel representation. The method was evaluated on the publicly available DRIVE database, widely used for this purpose, since they contain retinal images where the vascular structure has been precisely marked by experts.
 
The effectiveness and robustness of the method is proven with different image conditions. Together with its simplicity and fast implementation, everything make this blood vessel segmentation proposal suitable for retinal image  computer analyses such as automated screening for early diabetic retinopathy detection.
References: 

Marin, D., Aquino, A., Gegundez-Arias, M.E., Bravo, J.M., A New Supervised Method for Blood Vessel Segmentation in Retinal Images by Using Gray-Level and Moment Invariants-Based Features, Medical Imaging, IEEE Transactions on , vol.30, no.1, pp.146,158, Jan. 2011. doi: 10.1109/TMI.2010.2064333