Retinal prosthesis edge detection (RPED) algorithm: Low-power and improved visual acuity strategy for artificial retinal implants.


Journal

PloS one
ISSN: 1932-6203
Titre abrégé: PLoS One
Pays: United States
ID NLM: 101285081

Informations de publication

Date de publication:
2024
Historique:
received: 22 11 2023
accepted: 23 05 2024
medline: 18 6 2024
pubmed: 18 6 2024
entrez: 18 6 2024
Statut: epublish

Résumé

This paper proposes a retinal prosthesis edge detection (RPED) algorithm that can achieve high visual acuity and low power. Retinal prostheses have been used to stimulate retinal tissue by injecting charge via an electrode array, thereby artificially restoring the vision of visually impaired patients. The retinal prosthetic chip, which generates biphasic current pulses, should be located in the foveal area measuring 5 mm × 5 mm. When a high-density stimulation pixel array is realized in a limited area, the distance between the stimulation pixels narrows, resulting in current dispersion and high-power dissipation related to heat generation. Various edge detection methods have been proposed over the past decade to reduce these deleterious effects and achieve high-resolution pixels. However, conventional methods have the disadvantages of high-power consumption and long data processing times because many pixels are activated to detect edges. In this study, we propose a novel RPED algorithm that has a higher visual acuity and less power consumption despite using fewer active pixels than existing techniques. To verify the performance of the devised RPED algorithm, the peak signal-to-noise ratio and structural similarity index map, which evaluates the quantitative numerical value of the image are employed and compared with the Sobel, Canny, and past edge detection algorithms in MATLAB. Finally, we demonstrate the effectiveness of the proposed RPED algorithm using a 1600-pixel retinal stimulation chip fabricated using a 0.35 μm complementary metal-oxide-semiconductor process.

Identifiants

pubmed: 38889114
doi: 10.1371/journal.pone.0305132
pii: PONE-D-23-39009
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

e0305132

Informations de copyright

Copyright: © 2024 Oh et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Déclaration de conflit d'intérêts

The authors hace declared that no competing interests exist.

Auteurs

Yeonji Oh (Y)

Department of Medical Science, Korea University, Seoul, South Korea.

Jonggi Hong (J)

Department of Health Sciences & Technology, Gachon Advanced Institute for Health Sciences & Technology, Gachon University, Incheon, South Korea.

Jungsuk Kim (J)

Department of Biomedical Engineering, Gachon University, Sungnam, South Korea.
Cellico Research and Development Laboratory, Sungnam, South Korea.

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Classifications MeSH