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Automated methods in the diagnosing of retinal images
KTH, School of Technology and Health (STH), Health Systems Engineering, Systems Safety and Management.
2012 (English)Independent thesis Basic level (professional degree), 10 credits / 15 HE creditsStudent thesis
Abstract [en]

This report contains a summation of a variety of articles that have been read and analysed. Each article describes different methods that can be used to detect lesions, optic disks, drusen and exudates in retinal images. I.e. diagnose e.g. Diabetic Retinopathy and Age-Related Macular Degeneration. A general approach is presented, which all methods more or less is based on.

Methods to locate the optic disk

  • The PCA 
  • kNN Regression
  • Hough Transform
  • Fuzzy Convergence
  • Vessel Direction Matched Filter
  • Etc.

The best method based on result, reliability, number of images and publisher is kNN regression. The result of this method is remarkably good and that brings some doubt about its reliability. Though the method was published at IEEE and that gives the method a more trustful look. A next best method which also is very useful is Vessel Direction Matched Filter.

Methods to detect drusen – diagnose Age-Related Macular Degeneration

  • PNN classifier
  • Histogram approach
  • Etc.

The best method based on result, reliability, number of images and publisher is the PNN classifier. The method had a sensitivity of 94 % and a specificity of 95 %. 300 images were used in the experiment which was published by the IEEE in 2011.

Methods to detect exudates – diagnose Diabetic Retinopathy

  • Morphological techniques
  • Luv colour space, Wiener filter an Canny edge detector.

The best method based on result, reliability, number of images and publisher is an experiment called “Feature Extraction”. The method includes the Luv colour space, Wiener filter (remove noise) and the Canny edge detector.

Abstract [sv]

Den här rapporten innehåller en sammanfattning av ett flertal artiklar som har blivit studerade. Varje artikel har beskrivit en metod som kan användas för att upptäcka sjuka förändringar i ögonbottenbilder, det vill säga, åldersförändringar i gula fläcken och diabetisk retinopati.

Metoder för att lokalisera blinda fläcken

  • PCA
  • kNN regression
  • Hough omvandling
  • Suddig konvergens
  • Filtrering beroende på kärlens riktning
  • Mm.

Den bästa metoden baserat på resultat, pålitlighet, antal bilder och utgivare är kNN regression. De förvånansvärt goda resultaten kan inbringa lite tvivel på huruvida resultaten stämmer. Artikeln publicerades dock av IEEE och det gör artikeln mer trovärdig. Den näst bästa metoden är filtrering beroende på kärlens riktning.

Metoder för att diagnosticera åldersförändringar i gula fläcken

  • PNN klassificeraren
  • Histogram
  • Mm.

Den bästa metoden baserat på resultat, pålitlighet, antal bilder och utgivare är PNN klassificeraren. Metoden hade en sensitivitet på 94 % och en specificitet på 95 %. 300 bilder användes i experimentet som publicerades av IEEE år 2011.

Metoder att diagnosticera diabetisk retinopati

  • Morfologiska tekniker
  • Luv colour space, Wiener filter and Canny edge detector.

Den bästa metoden baserat på resultat, pålitlighet, antal bilder och utgivare är ett experimentet som heter ”Feature Extraction”. Experimentet inkluderar Luv colour space, Wiener filter (brus borttagning) och Canny edge detector

Place, publisher, year, edition, pages
2012.
Series
TRITA-STH ; 2012:5
Keywords [en]
retina, fundus, automatic, automated, analysis, image, optic disk, macula, agerelated macular degeneration, drusen, diabetic retinopathy and diagnoses.
National Category
Medical Image Processing
Identifiers
URN: urn:nbn:se:kth:diva-122721OAI: oai:DiVA.org:kth-122721DiVA, id: diva2:623529
Educational program
Bachelor of Science in Engineering - Medical Technology
Supervisors
Examiners
Available from: 2019-09-11 Created: 2013-05-27 Last updated: 2019-09-11Bibliographically approved

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