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Cited by 10Retinal Imaging and AnalysisMedical Image Segmentation TechniquesRetinal Diseases and Treatments

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Detecting red-lesions from retinal fundus images using unique morphological features

Open access
Monemian, Maryam, Rabbani, Hossein·2023·Scientific Reports
DOI 10.1038/s41598-023-30459-5PMC PMC9977778
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Abstract

One of the most important retinal diseases is Diabetic Retinopathy (DR) which can lead to serious damage to vision if remains untreated. Red-lesions are from important demonstrations of DR helping its identification in early stages. The detection and verification of them is helpful in the evaluation of disease severity and progression. In this paper, a novel image processing method is proposed for extracting red-lesions from fundus images. The method works based on finding and extracting the unique morphological features of red-lesions. After quality improvement of images, a pixel-based verification is performed in the proposed method to find the ones which provide a significant intensity change in a curve-like neighborhood. In order to do so, a curve is considered around each pixel and the intensity changes around the curve boundary are considered. The pixels for which it is possible to find such curves in at least two directions are considered as parts of red-lesions. The simplicity of computations, the high accuracy of results, and no need to post-processing operations are the important characteristics of the proposed method endorsing its good performance.

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Crossref · 10.1038/s41598-023-30459-5

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Figure 1 Different circle quarters centered at pi,j.
Figure 1 Different circle quarters centered at pi,j.CC BY · PubMed Central
Figure 2 Example of a circle quarter and corresponding points along two certain angles.
Figure 2 Example of a circle quarter and corresponding points along two certain angles.CC BY · PubMed Central
Figure 3 A schematic for pi,j and its roundish neighborhood in (a) top-right and (b) top-left, (c) bottom-left and (d) bottom-right parts.
Figure 3 A schematic for pi,j and its roundish neighborhood in (a) top-right and (b) top-left, (c) bottom-left and (d) bottom-right parts.CC BY · PubMed Central
Figure 4 Pseudo-code for pixel-wise verification.
Figure 4 Pseudo-code for pixel-wise verification.CC BY · PubMed Central
Figure 5 (a) A retinal fundus image, (b) the extracted red-lesions.
Figure 5 (a) A retinal fundus image, (b) the extracted red-lesions.CC BY · PubMed Central
Figure 6 The effect of two values of diff_th on the capability of red-lesion detection.
Figure 6 The effect of two values of diff_th on the capability of red-lesion detection.CC BY · PubMed Central
Figure 7 The effect of two values of Intensity_th on the capability of red-lesion detection.
Figure 7 The effect of two values of Intensity_th on the capability of red-lesion detection.CC BY · PubMed Central

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Citations

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Research works printed but not linked: 37

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Cited by

Metadata

Authors
Monemian, M., & Rabbani, H.
Affiliations
Isfahan University of Medical Sciences (IR)
Corresponding author
Rabbani, Hossein
ORCID
Venue
Scientific Reports
Publisher
Springer Science and Business Media LLC
Published
2023-03-01
Volume
13
Issue
1
Pages
3487-3487
ISSN
2045-2322 (ISSN-L)
DOI
10.1038/s41598-023-30459-5
PMC
PMC9977778
OpenAlex
W4322733455
Cited by
10
References
2
Citations by year
2026: 4 · 2025: 1 · 2024: 2 · 2023: 3
FWCI
1.66
Type
journal article
Language
en
Open access
Licence
Locations
  • Scientific Reports (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Topics
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Retinal Imaging and Analysis
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
  • Health Sciences › Medicine › Ophthalmology › Retinal Diseases and Treatments
Keywords
diabetic retinopathy, fundus images, morphological features, lesion segmentation, early disease detection
MeSH terms
Abomasum, Animals, Diabetic Retinopathy, Fundus Oculi, Image Processing, Computer-Assisted, Patient Acuity

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