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Detection and Mosaicing Techniques for Low-Quality Retinal Videos

Camara, J. ; Silva, B. ; Gouveia, A. ; Pires, I.M.P. ; Coelho, P. ; Cunha, A.

Sensors Vol. 22, Nº 5, pp. 2059 - 2059, March, 2022.

ISSN (print): 1424-3210
ISSN (online): 1424-8220

Scimago Journal Ranking: 0,80 (in 2021)

Digital Object Identifier: 10.3390/s22052059

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Abstract
Ideally, to carry out screening for eye diseases, it is expected to use specialized medical equipment to capture retinal fundus images. However, since this kind of equipment is generally
expensive and has low portability, and with the development of technology and the emergence of smartphones, new portable and cheaper screening options have emerged, one of them being
the D-Eye device. When compared to specialized equipment, this equipment and other similar devices associated with a smartphone present lower quality and less field-of-view in the retinal video
captured, yet with sufficient quality to perform a medical pre-screening. Individuals can be referred for specialized screening to obtain a medical diagnosis if necessary. Two methods were proposed to extract the relevant regions from these lower-quality videos (the retinal zone). The first one is
based on classical image processing approaches such as thresholds and Hough Circle transform. The other performs the extraction of the retinal location by applying a neural network, which is one of the methods reported in the literature with good performance for object detection, the YOLO v4, which
was demonstrated to be the preferred method to apply. A mosaicing technique was implemented from the relevant retina regions to obtain a more informative single image with a higher field of view. It was divided into two stages: the GLAMpoints neural network was applied to extract relevant points in the first stage. Some homography transformations are carried out to have in the same referential the overlap of common regions of the images. In the second stage, a smoothing process was performed in the transition between images.