Automated localization of cysts in diabetic macular edema using optical coherence tomography images

Sohini Roychowdhury, Dara Koozekanani, Salma Radwan, Keshab K Parhi

Research output: Contribution to journalArticle

17 Citations (Scopus)

Abstract

This paper presents a novel automated system that localizes cysts in optical coherence tomography (OCT) images of patients with diabetic macular edema (DME). First, in each image, six sub-retinal layers are detected using an iterative high-pass filtering approach. Next, significantly dark regions within the retinal micro-structure are detected as candidate cystoid regions. Each candidate cystoid region is then further analyzed using solidity, mean and maximum pixel value of the negative OCT image as decisive features for estimating the area of cystoid regions. The proposed system achieves 90% correlation between the estimated cystoid area and the manually marked area, and a mean error of 4.6%. Finally the proposed algorithm locates the cysts in the inner plexiform region, inner nuclear region and outer nuclear region with an accuracy of 88%, 86% and 80%, respectively.

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Macular Edema
Optical tomography
Optical Coherence Tomography
Cysts
Pixels
Microstructure

Keywords

  • correlation coefficient
  • cystoid area
  • diabetic macular edema
  • iterative segmentation
  • Optical coherence tomography

PubMed: MeSH publication types

  • Journal Article

Cite this

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title = "Automated localization of cysts in diabetic macular edema using optical coherence tomography images",
abstract = "This paper presents a novel automated system that localizes cysts in optical coherence tomography (OCT) images of patients with diabetic macular edema (DME). First, in each image, six sub-retinal layers are detected using an iterative high-pass filtering approach. Next, significantly dark regions within the retinal micro-structure are detected as candidate cystoid regions. Each candidate cystoid region is then further analyzed using solidity, mean and maximum pixel value of the negative OCT image as decisive features for estimating the area of cystoid regions. The proposed system achieves 90{\%} correlation between the estimated cystoid area and the manually marked area, and a mean error of 4.6{\%}. Finally the proposed algorithm locates the cysts in the inner plexiform region, inner nuclear region and outer nuclear region with an accuracy of 88{\%}, 86{\%} and 80{\%}, respectively.",
keywords = "correlation coefficient, cystoid area, diabetic macular edema, iterative segmentation, Optical coherence tomography",
author = "Sohini Roychowdhury and Dara Koozekanani and Salma Radwan and Parhi, {Keshab K}",
year = "2013",
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AU - Roychowdhury, Sohini

AU - Koozekanani, Dara

AU - Radwan, Salma

AU - Parhi, Keshab K

PY - 2013

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N2 - This paper presents a novel automated system that localizes cysts in optical coherence tomography (OCT) images of patients with diabetic macular edema (DME). First, in each image, six sub-retinal layers are detected using an iterative high-pass filtering approach. Next, significantly dark regions within the retinal micro-structure are detected as candidate cystoid regions. Each candidate cystoid region is then further analyzed using solidity, mean and maximum pixel value of the negative OCT image as decisive features for estimating the area of cystoid regions. The proposed system achieves 90% correlation between the estimated cystoid area and the manually marked area, and a mean error of 4.6%. Finally the proposed algorithm locates the cysts in the inner plexiform region, inner nuclear region and outer nuclear region with an accuracy of 88%, 86% and 80%, respectively.

AB - This paper presents a novel automated system that localizes cysts in optical coherence tomography (OCT) images of patients with diabetic macular edema (DME). First, in each image, six sub-retinal layers are detected using an iterative high-pass filtering approach. Next, significantly dark regions within the retinal micro-structure are detected as candidate cystoid regions. Each candidate cystoid region is then further analyzed using solidity, mean and maximum pixel value of the negative OCT image as decisive features for estimating the area of cystoid regions. The proposed system achieves 90% correlation between the estimated cystoid area and the manually marked area, and a mean error of 4.6%. Finally the proposed algorithm locates the cysts in the inner plexiform region, inner nuclear region and outer nuclear region with an accuracy of 88%, 86% and 80%, respectively.

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KW - cystoid area

KW - diabetic macular edema

KW - iterative segmentation

KW - Optical coherence tomography

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