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2017 | OriginalPaper | Buchkapitel

Segmentation of Retinal Blood Vessels Using Dictionary Learning Techniques

verfasst von : Taibou Birgui Sekou, Moncef Hidane, Julien Olivier, Hubert Cardot

Erschienen in: Fetal, Infant and Ophthalmic Medical Image Analysis

Verlag: Springer International Publishing

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Abstract

In this paper, we aim at proving the effectiveness of dictionary learning techniques on the task of retinal blood vessel segmentation. We present three different methods based on dictionary learning and sparse coding that reach state-of-the-art results. Our methods are tested on two, well-known, publicly available datasets: DRIVE and STARE. The methods are compared to many state-of-the-art approaches and turn out to be very promising.

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Fußnoten
1
The \(\ell _q\)-norm (\(q \ge 1\)) of a vector \(\mathbf {x}\) is: \(\Vert \mathbf {x}\Vert _q = [ \sum _i \mid x[i]\mid ^q ]^{1/q}\).
 
2
The Frobenius-norm of a matrix \(\mathbf {A} \in \mathbb {R}^{m\times n}\) is: \(\Vert \mathbf {A}\Vert _F = \big [\sum _{i=1}^{m} \sum _{j=1}^{n} A[i,j]^2\big ]^{1/2}\).
 
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Metadaten
Titel
Segmentation of Retinal Blood Vessels Using Dictionary Learning Techniques
verfasst von
Taibou Birgui Sekou
Moncef Hidane
Julien Olivier
Hubert Cardot
Copyright-Jahr
2017
DOI
https://doi.org/10.1007/978-3-319-67561-9_9

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