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(Reference retrieved automatically from SciELO through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Algorithm for predicting macular dysfunction based on moment invariants classification of the foveal avascular zone in functional retinal images

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Author(s):
Angélica Moises Arthur [1] ; Rangel Arthur [2] ; Alexandre Gonçalves Silva [3] ; Marina Silva Fouto [4] ; Yuzo Iano [5] ; Jacqueline Mendonça Lopes de Faria [6]
Total Authors: 6
Affiliation:
[1] University of Campinas. Faculty of Electrical and Computer Engineering - Brasil
[2] University of Campinas. Faculty of Technology - Brasil
[3] Federal University of Santa Catarina. Department of Informatics and Statistics - Brasil
[4] Santa Catarina State University. Department of Computer Science - Brasil
[5] University of Campinas. Faculty of Electrical and Computer Engineering - Brasil
[6] University of Campinas. Faculty of Medical Sciences - Brasil
Total Affiliations: 6
Document type: Journal article
Source: Res. Biomed. Eng.; v. 33, n. 4, p. 344-351, 2017-12-21.
Abstract

Abstract Introduction A new method for segmenting and quantifying the macular area based on morphological alternating sequential filtering (ASF) is proposed. Previous studies show that persons with diabetes present alterations in the foveal avascular zone (FAZ) prior to the appearance of retinopathy. Thus, a proper characterization of FAZ using a method of automatic classification and prediction is a supportive and complementary tool for medical evaluation of the macular region, and may be useful for possible early treatment of eye diseases in persons without diabetic retinopathy. Methods We obtained high-resolution retinal images using a non-invasive functional imaging system called Retinal Function Imager to generate a series of combined capillary perfusion maps. We filtered sequentially the macular images to reduce the complexity by ASF. Then we segmented the FAZ using watershed transform from an automatic selection of markers. Using Hu’s moment invariants as a descriptor, we can automatically classify and categorize each FAZ. Results The FAZ differences between non-diabetic volunteers and diabetic subjects were automatically distinguished by the proposed system with an accuracy of 81%. Conclusion This is an innovative method to classify FAZ using a fully automatic algorithm for segmentation (based on morphological operators) and for the classification (based on descriptor formed by Hu’s moments) despite the presence of edema or other structures. This is an alternative tool for eye exams, which may contribute to the analysis and evaluation of FAZ morphology, promoting the prevention of macular impairment in diabetics without retinopathy. (AU)

FAPESP's process: 14/22687-0 - Contribution of AMPK pathway to renal fibrosis and pathogenesis of diabetic nephropathy and retinopathy
Grantee:Jose Butori Lopes de Faria
Support Opportunities: Research Projects - Thematic Grants