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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Notes
- 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: \(\Vert \mathbf {A}\Vert _F = \big [\sum _{i=1}^{m} \sum _{j=1}^{n} A[i,j]^2\big ]^{1/2}\).
- 3.
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Birgui Sekou, T., Hidane, M., Olivier, J., Cardot, H. (2017). Segmentation of Retinal Blood Vessels Using Dictionary Learning Techniques. In: Cardoso, M., et al. Fetal, Infant and Ophthalmic Medical Image Analysis. OMIA FIFI 2017 2017. Lecture Notes in Computer Science(), vol 10554. Springer, Cham. https://doi.org/10.1007/978-3-319-67561-9_9
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DOI: https://doi.org/10.1007/978-3-319-67561-9_9
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