This paper proposes a novel method for modeling retinal cone distribution in humans. It is based on Blue-noise sampling algorithms being strongly related with the mosaic sampling performed by cone photoreceptors in the human retina. Here we present the method together with a series of examples of various real retinal patches. The same samples have also been created with alternative algorithms and compared with plots of the center of the inner segments of cone photoreceptors from imaged retinas. Results are evaluated with different distance measure used in the field, like nearest-neighbor analysis and pair correlation function. The proposed method can effectively describe features of a human retinal cone distribution by allowing to create samples similar to the available data. For this reason, we believe that the proposed algorithm may be a promising solution when modeling local patches of retina.

Blue-noise sampling for human retinal cone spatial distribution modeling / M.P. Lanaro, H. Perrier, D. Coeurjolly, V. Ostromoukhov, A. Rizzi. - In: JOURNAL OF PHYSICS COMMUNICATIONS. - ISSN 2399-6528. - 4:3(2020 Mar 30), pp. 035013.1-035013.13. [10.1088/2399-6528/ab8064]

Blue-noise sampling for human retinal cone spatial distribution modeling

M.P. Lanaro
Primo
;
A. Rizzi
Ultimo
2020-03-30

Abstract

This paper proposes a novel method for modeling retinal cone distribution in humans. It is based on Blue-noise sampling algorithms being strongly related with the mosaic sampling performed by cone photoreceptors in the human retina. Here we present the method together with a series of examples of various real retinal patches. The same samples have also been created with alternative algorithms and compared with plots of the center of the inner segments of cone photoreceptors from imaged retinas. Results are evaluated with different distance measure used in the field, like nearest-neighbor analysis and pair correlation function. The proposed method can effectively describe features of a human retinal cone distribution by allowing to create samples similar to the available data. For this reason, we believe that the proposed algorithm may be a promising solution when modeling local patches of retina.
Blue-noise sampling; Cones spatial distribution; Retina modeling; Stochastic Point processes
Settore ING-INF/05 - Sistemi di Elaborazione delle Informazioni
Settore INF/01 - Informatica
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/2434/861483
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