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Topological Data Analysis for Eye Fundus Image Quality Assessment.

Gener José Avilés-Rodríguez1, Juan Iván Nieto-Hipólito1, María de Los Ángeles Cosío-León2

  • 1Facultad de Ingeniería Arquitectura y Diseño, Universidad Autónoma de Baja California, Carretera Transpeninsular Ensenada-Tijuana #3917, Playitas, Ensenada 22860, Mexico.

Diagnostics (Basel, Switzerland)
|August 27, 2021
PubMed
Summary
This summary is machine-generated.

Topological data analysis and machine learning effectively assess eye fundus image quality for digital fundoscopy. This method provides accurate classification, aiding automated eye exams and computer-aided diagnosis systems.

Keywords:
computational ophthalmologyeye fundus imagesimage quality assessmentpersistent homologytopological data analysis

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Area of Science:

  • Medical Imaging
  • Computer Vision
  • Data Science

Background:

  • Eye health accessibility is a global challenge.
  • Digital tools for automated eye exams are crucial.
  • Image quality assessment (IQA) is vital for digital fundoscopy and computer-aided diagnosis (CAD).

Purpose of the Study:

  • To perform IQA on eye fundus images using topological data analysis (TDA) and machine learning.
  • To evaluate the effectiveness of TDA-derived features for classifying image quality.

Main Methods:

  • Utilized cubical complexes to represent eye fundus images from the EyePACS dataset.
  • Calculated persistent homology and generated persistence diagrams.
  • Extracted 30 vectorized topological descriptors for classification using logistic regression (LoGit).

Main Results:

  • Achieved a global accuracy of 0.932 on the validation subset.
  • Demonstrated high precision (0.912-0.952) and recall (0.912-0.932) for quality and no quality labels.
  • Obtained an AUC of 0.980 and an F1 score of 0.932, indicating robust classification performance.

Conclusions:

  • Topological methods are effective for IQA in digital fundoscopy.
  • A small set of topological descriptors can yield clinically useful classification results.
  • This approach supports the development of robust CAD systems for eye health.