Cargando…
Topological Data Analysis for Eye Fundus Image Quality Assessment
The objective of this work is to perform image quality assessment (IQA) of eye fundus images in the context of digital fundoscopy with topological data analysis (TDA) and machine learning methods. Eye health remains inaccessible for a large amount of the global population. Digital tools that automiz...
Autores principales: | , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8394537/ https://www.ncbi.nlm.nih.gov/pubmed/34441257 http://dx.doi.org/10.3390/diagnostics11081322 |
_version_ | 1783743971127721984 |
---|---|
author | Avilés-Rodríguez, Gener José Nieto-Hipólito, Juan Iván Cosío-León, María de los Ángeles Romo-Cárdenas, Gerardo Salvador Sánchez-López, Juan de Dios Radilla-Chávez, Patricia Vázquez-Briseño, Mabel |
author_facet | Avilés-Rodríguez, Gener José Nieto-Hipólito, Juan Iván Cosío-León, María de los Ángeles Romo-Cárdenas, Gerardo Salvador Sánchez-López, Juan de Dios Radilla-Chávez, Patricia Vázquez-Briseño, Mabel |
author_sort | Avilés-Rodríguez, Gener José |
collection | PubMed |
description | The objective of this work is to perform image quality assessment (IQA) of eye fundus images in the context of digital fundoscopy with topological data analysis (TDA) and machine learning methods. Eye health remains inaccessible for a large amount of the global population. Digital tools that automize the eye exam could be used to address this issue. IQA is a fundamental step in digital fundoscopy for clinical applications; it is one of the first steps in the preprocessing stages of computer-aided diagnosis (CAD) systems using eye fundus images. Images from the EyePACS dataset were used, and quality labels from previous works in the literature were selected. Cubical complexes were used to represent the images; the grayscale version was, then, used to calculate a persistent homology on the simplex and represented with persistence diagrams. Then, 30 vectorized topological descriptors were calculated from each image and used as input to a classification algorithm. Six different algorithms were tested for this study (SVM, decision tree, k-NN, random forest, logistic regression (LoGit), MLP). LoGit was selected and used for the classification of all images, given the low computational cost it carries. Performance results on the validation subset showed a global accuracy of 0.932, precision of 0.912 for label “quality” and 0.952 for label “no quality”, recall of 0.932 for label “quality” and 0.912 for label “no quality”, AUC of 0.980, F1 score of 0.932, and a Matthews correlation coefficient of 0.864. This work offers evidence for the use of topological methods for the process of quality assessment of eye fundus images, where a relatively small vector of characteristics (30 in this case) can enclose enough information for an algorithm to yield classification results useful in the clinical settings of a digital fundoscopy pipeline for CAD. |
format | Online Article Text |
id | pubmed-8394537 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-83945372021-08-28 Topological Data Analysis for Eye Fundus Image Quality Assessment Avilés-Rodríguez, Gener José Nieto-Hipólito, Juan Iván Cosío-León, María de los Ángeles Romo-Cárdenas, Gerardo Salvador Sánchez-López, Juan de Dios Radilla-Chávez, Patricia Vázquez-Briseño, Mabel Diagnostics (Basel) Article The objective of this work is to perform image quality assessment (IQA) of eye fundus images in the context of digital fundoscopy with topological data analysis (TDA) and machine learning methods. Eye health remains inaccessible for a large amount of the global population. Digital tools that automize the eye exam could be used to address this issue. IQA is a fundamental step in digital fundoscopy for clinical applications; it is one of the first steps in the preprocessing stages of computer-aided diagnosis (CAD) systems using eye fundus images. Images from the EyePACS dataset were used, and quality labels from previous works in the literature were selected. Cubical complexes were used to represent the images; the grayscale version was, then, used to calculate a persistent homology on the simplex and represented with persistence diagrams. Then, 30 vectorized topological descriptors were calculated from each image and used as input to a classification algorithm. Six different algorithms were tested for this study (SVM, decision tree, k-NN, random forest, logistic regression (LoGit), MLP). LoGit was selected and used for the classification of all images, given the low computational cost it carries. Performance results on the validation subset showed a global accuracy of 0.932, precision of 0.912 for label “quality” and 0.952 for label “no quality”, recall of 0.932 for label “quality” and 0.912 for label “no quality”, AUC of 0.980, F1 score of 0.932, and a Matthews correlation coefficient of 0.864. This work offers evidence for the use of topological methods for the process of quality assessment of eye fundus images, where a relatively small vector of characteristics (30 in this case) can enclose enough information for an algorithm to yield classification results useful in the clinical settings of a digital fundoscopy pipeline for CAD. MDPI 2021-07-23 /pmc/articles/PMC8394537/ /pubmed/34441257 http://dx.doi.org/10.3390/diagnostics11081322 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Avilés-Rodríguez, Gener José Nieto-Hipólito, Juan Iván Cosío-León, María de los Ángeles Romo-Cárdenas, Gerardo Salvador Sánchez-López, Juan de Dios Radilla-Chávez, Patricia Vázquez-Briseño, Mabel Topological Data Analysis for Eye Fundus Image Quality Assessment |
title | Topological Data Analysis for Eye Fundus Image Quality Assessment |
title_full | Topological Data Analysis for Eye Fundus Image Quality Assessment |
title_fullStr | Topological Data Analysis for Eye Fundus Image Quality Assessment |
title_full_unstemmed | Topological Data Analysis for Eye Fundus Image Quality Assessment |
title_short | Topological Data Analysis for Eye Fundus Image Quality Assessment |
title_sort | topological data analysis for eye fundus image quality assessment |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8394537/ https://www.ncbi.nlm.nih.gov/pubmed/34441257 http://dx.doi.org/10.3390/diagnostics11081322 |
work_keys_str_mv | AT avilesrodriguezgenerjose topologicaldataanalysisforeyefundusimagequalityassessment AT nietohipolitojuanivan topologicaldataanalysisforeyefundusimagequalityassessment AT cosioleonmariadelosangeles topologicaldataanalysisforeyefundusimagequalityassessment AT romocardenasgerardosalvador topologicaldataanalysisforeyefundusimagequalityassessment AT sanchezlopezjuandedios topologicaldataanalysisforeyefundusimagequalityassessment AT radillachavezpatricia topologicaldataanalysisforeyefundusimagequalityassessment AT vazquezbrisenomabel topologicaldataanalysisforeyefundusimagequalityassessment |