Automated detection of red lesions in the presence of blood vessels in retinal fundus images using morphological operations

Kaur, Navkiran; Kaur, Jasmeen; Accharya, Mausumi; Gupta, Sheifali

doi:10.1109/icpeices.2016.7853561

Cited by 2 publications

(1 citation statement)

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“…In the Gabor transformation-based studies, the success rates were 90.24% on 120 images [16], 93.71% on 20 images [17], 93.1% on 20 images [18], 99.4% on 130 images [19], 98.12% on 1410 images [20], and 94.76% on 89 images [21], with the help of image processing methods such as color space transformation, adaptive histogram equalization, Gabor wavelet transformation, etc., respectively. In the studies based on statistical features, the success rates were 97.99% on 80 images [22], 73.8% on 301 images [23], 96.7% on 89 images [24], 97.2% on 1200 images [25], 89.23% on 50 images [26], 89% on 89 images [27], and 86% on 243 images [28] with the analysis of statistical features of images such as means, standard deviation, variance, contrast, and skewness after applying the color space transformations, contrast and brightness adjustment, and adaptive histogram equalization preprocessing to the retinal images. In the studies based on HSV color space transformation and the Gauss filter, the success rates were 86.62% on 60 images [29] and 92% on 143 images [30].…”

Section: Introductionmentioning

confidence: 99%

Detection of hemorrhage in retinal images using linear classifiers and iterative thresholding approaches based on firefly and particle swarm optimization algorithms

Adem¹,

Hekim²,

Demir³

2019

Turk J Elec Eng & Comp Sci

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We propose a novel iterative thresholding approach based on firefly and particle swarm optimization to be used for the detection of hemorrhages, one of the signs of diabetic retinopathy disease. This approach consists of the enhancement of the image using basic preprocessing methods, the segmentation of vessels with the help of Gabor and Top-hat transformation for the removal of the vessels from the image, the determination of the number of regions with hemorrhages and pixel counts in these regions using firefly algorithm (FFA) and particle swarm optimization algorithm (PSOA)-based iterative thresholding, and the detection of hemorrhages with the help of a support vector machine (SVM) and linear regression (LR)-based classifier. In the preprocessing step, color space selection, brightness and contrast adjustment, and adaptive histogram equalization are applied to enhance retinal images, respectively. In the step of segmentation, blood vessels are detected by using Gabor and Top-hat transformations and are removed from the image to avoid confusion with hemorrhagic regions in the retinal image. In the iterative thresholding step, the number of hemorrhagic regions and pixel counts in these regions are determined by using an iterative thresholding approach that generates different thresholding values with the FFA/PSOA. In the classification step, the hemorrhagic regions and pixel counts obtained by the iterative thresholding are used as inputs in the LR/SVM-based classifier. PSOA-based iterative thresholding and the SVM classifier achieved 96.7% sensitivity, 91.4% specificity, and 94.1% accuracy for hemorrhage detection. Finally, the experiments show that the correct classification rates and time performances of the PSOA-based iterative thresholding algorithm are better than those of the FFA in hemorrhage detection. In addition, the proposed approach can be used as a diagnostic decision support system for detecting hemorrhages with high success rate.

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