Metaheuristic Techniques for Detection of Optic Disc in Retinal Fundus Images

Pruthi, Jyotika; Arora, Shaveta; Khanna, Kavita

doi:10.1007/s13319-018-0198-3

Cited by 9 publications

(5 citation statements)

References 31 publications

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“…To take advantage of these variations, many researchers have used metaheuristic algorithms for optic disc detection in retinal fundus images. Pruthi et al [2] proposed some metaheuristic algorithms, namely Ant Colony Optimization Algorithm, Bacterial Foraging Optimization Algorithm, Firefly Algorithm, Cuckoo Search Algorithm, and Krill Herd Algorithms. Kumar et al [5] used the Jaya algorithm to solve the problem of localizing the optic disc in retinal images.…”

Section: Optic Disc Localizationmentioning

confidence: 99%

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Optic Disc Segmentation in Human Retina Images Using a Meta Heuristic Optimization Method and Disease Diagnosis with Deep Learning

Almeshrky,

Karacı

2024

Applied Sciences

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Glaucoma is a common eye disease that damages the optic nerve and leads to loss of vision. The disease shows few symptoms in the early stages, making its identification a complex task. To overcome the challenges associated with this task, this study aimed to tackle the localization and segmentation of the optic disc, as well as the classification of glaucoma. For the optic disc segmentation, we propose a novel metaheuristic approach called Grey Wolf Optimization (GWO). Two different approaches are used for glaucoma classification: a one-stage approach, in which the whole image without cropping is used for classification, and a two-stage approach. In the two-stage approach, the optic disc region is detected using the You Only Look Once (YOLO) detection algorithm. Once the optic disc region of interest (ROI) is identified, glaucoma classification is performed using pre-trained convolutional neural networks (CNNs) and vision transformation techniques. In addition, both the one-stage and the two-stage approaches are applied in combination with the pre-trained CNN using the Random Forest algorithm. In segmentation, GWO achieved an average sensitivity of 96.04%, a specificity of 99.58%, an accuracy of 99.39%, a DICE coefficient of 94.15%, and a Jaccard index of 90.4% on the Drishti-GS dataset. For classification, the proposed method achieved remarkable results with a high-test accuracy of 100% and 88.18% for hold-out validation and three-fold cross-validation for the Drishti-GS dataset, and 96.15% and 93.84% for ORIGA with hold-out and five-fold cross-validation, respectively. Comparing the results with previous studies, the proposed CNN model outperforms them. In addition, the use of the Swin transformer shows its effectiveness in classifying glaucoma in different subsets of the data.

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Section: Optic Disc Localizationmentioning

confidence: 99%

“…If left untreated, it can lead to extensive damage to the optic nerves, resulting in loss of vision. The biggest challenge in detecting these eye diseases is the absence of outward symptoms until vision loss has already occurred [2]. There are no warning signs in the early stages.…”

Section: Introductionmentioning

confidence: 99%

Optic Disc Segmentation in Human Retina Images Using a Meta Heuristic Optimization Method and Disease Diagnosis with Deep Learning

Almeshrky,

Karacı

2024

Applied Sciences

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“…The method had an average ACC of 100% and 97.70% in DRIVE and DiaretDB1. Pruthi et al [15] used the ant colony optimization and Cuckoo search technique to detect OD. The initial stage in their study was the median filtering on the RGB (Red, Green and Blue) channel of the input image, followed by contrast enhancement using the adaptive histogram equalization technique.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Xiong et al [3] Simple thresholding operation and mathematical morphology method 97.80% Pernil et al [4] Hough circle cloud method 99.60% Hou et al [14] Template matching 97.70% Abdullah et al [30] Active contour model evolution based on FCM method 97.53% Proposed method FKMT-MOPDF 97.11% Automatic thresholding 96.30% Gui et al [13] Improved corner detection technique 86.40% Pruthi et al [15] Ant colony optimization and cuckoo search method 98.00% Proposed method FKMT-MOPDF 97.47%…”

Section: Authorsmentioning

confidence: 99%

Automatic Optic Disc Detection in Retinal Images Using FKMT‒MOPDF

Wisaeng¹

2023

Computer Systems Science and Engineering

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In recent days, detecting Optic Disc (OD) in retinal images has been challenging and very important to the early diagnosis of eye diseases. The process of detecting the OD is challenging due to the diversity of color, intensity, brightness and shape of the OD. Moreover, the color similarities of the neighboring organs of the OD create difficulties during OD detection. In the proposed Fuzzy K-Means Threshold (FKMT) and Morphological Operation with Pixel Density Feature (MOPDF), the input retinal images are coarsely segmented by fuzzy K-means clustering and thresholding, in which the OD is classified from its neighboring organs with intensity similarities. Then, the segmented images are given as the input to morphological operation with pixel density feature calculations, which reduce the false detection in the small pixel of the OD. Finally, the OD area is detected by applying the Sobel edge detection method, which accurately detects the OD from the retinal images. After detection optimization, the proposed method achieved Sensitivity (SEN), Specificity (SPEC) and Accuracy (ACC), with 96.74%, 96.78% and 96.92% in DiaretDB0 (Standard Diabetic Retinopathy Database Calibration level 0), 97.12%, 97.10% and 97.75% in Dia-retDB1 (Standard Diabetic Retinopathy Database Calibration level 1) and 97.19%, 97.47% and 97.43% in STARE (Structured Analysis of the Retina) dataset respectively. The experimental results demonstrated the proposed method's efficiency for segmenting and detecting OD areas.

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“…Despite the availability of various FS techniques, metaheuristic algorithms (MAs) have gained traction for their adaptability and efficacy in optimizing FS processes. These algorithms, including the genetic algorithm (GA) [ 18 ], the slap swarm algorithm (SSA) [ 19 ], particle swarm optimization (PSO) and artificial bee colony (ABC) [ 20 ], the golden jackal optimization algorithm (GJO) [ 21 ], and the bacterial foraging optimization algorithm (BFO) [ 22 , 23 ], exhibit remarkable flexibility in addressing the complexities inherent in glaucoma classification tasks. As FS can be viewed as an optimization challenge, there are no single metaheuristic algorithms capable of addressing all the complexities involved.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Medical Image Classification with an Advanced Feature Selection Algorithm: A Novel Approach to Improving the Cuckoo Search Algorithm by Incorporating Caputo Fractional Order

Habeb,

Taresh,

et al. 2024

Diagnostics

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Glaucoma is a chronic eye condition that seriously impairs vision and requires early diagnosis and treatment. Automated detection techniques are essential for obtaining a timely diagnosis. In this paper, we propose a novel method for feature selection that integrates the cuckoo search algorithm with Caputo fractional order (CFO-CS) to enhance the performance of glaucoma classification. However, when using the infinite series, the Caputo definition has memory length truncation issues. Therefore, we suggest a fixed memory step and an adjustable term count for optimization. We conducted experiments integrating various feature extraction techniques, including histograms of oriented gradients (HOGs), local binary patterns (LBPs), and deep features from MobileNet and VGG19, to create a unified vector. We evaluate the informative features selected from the proposed method using the k-nearest neighbor. Furthermore, we use data augmentation to enhance the diversity and quantity of the training set. The proposed method enhances convergence speed and the attainment of optimal solutions during training. The results demonstrate superior performance on the test set, achieving 92.62% accuracy, 94.70% precision, 93.52% F1-Score, 92.98% specificity, 92.36% sensitivity, and 85.00% Matthew’s correlation coefficient. The results confirm the efficiency of the proposed method, rendering it a generalizable and applicable technique in ophthalmology.

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Metaheuristic Techniques for Detection of Optic Disc in Retinal Fundus Images

Cited by 9 publications

References 31 publications

Optic Disc Segmentation in Human Retina Images Using a Meta Heuristic Optimization Method and Disease Diagnosis with Deep Learning

Optic Disc Segmentation in Human Retina Images Using a Meta Heuristic Optimization Method and Disease Diagnosis with Deep Learning

Automatic Optic Disc Detection in Retinal Images Using FKMT‒MOPDF

Enhancing Medical Image Classification with an Advanced Feature Selection Algorithm: A Novel Approach to Improving the Cuckoo Search Algorithm by Incorporating Caputo Fractional Order

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