Mr. Alessio Montuoro Profile

Alessio Montuoro

at Medizinische Univ Wien

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Proceedings Article | 20 March 2015 Paper

Rotation invariant eigenvessels and auto-context for retinal vessel detection

Alessio Montuoro, Christian Simader, Georg Langs, Ursula Schmidt-Erfurth

Proceedings Volume 9413, 94131F (2015) https://doi.org/10.1117/12.2081918

KEYWORDS: Image segmentation, Medical imaging, Eye, Image analysis, Receivers, Principal component analysis, Spinal cord, 3D image processing, Neuroimaging, Brain

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Retinal vessels are one of the few anatomical landmarks that are clearly visible in various imaging modalities of the eye. As they are also relatively invariant to disease progression, retinal vessel segmentation allows cross-modal and temporal registration enabling exact diagnosing for various eye diseases like diabetic retinopathy, hypertensive retinopathy or age-related macular degeneration (AMD). Due to the clinical significance of retinal vessels many different approaches for segmentation have been published in the literature. In contrast to other segmentation approaches our method is not specifically tailored to the task of retinal vessel segmentation. Instead we utilize a more general image classification approach and show that this can achieve comparable results. In the proposed method we utilize the concepts of eigenfaces and auto-context. Eigenfaces have been described quite extensively in the literature and their performance is well known. They are however quite sensitive to translation and rotation. The former was addressed by computing the eigenvessels in local image windows of different scales, the latter by estimating and correcting the local orientation. Auto-context aims to incorporate automatically generated context information into the training phase of classification approaches. It has been shown to improve the performance of spinal cord segmentation4 and 3D brain image segmentation. The proposed method achieves an area under the receiver operating characteristic (ROC) curve of A_z = 0.941 on the DRIVE data set, being comparable to current state-of-the-art approaches.

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