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

Adem, Kemal; Hekim, Mahmut; Demir, Selim

doi:10.3906/elk-1804-147

Cited by 27 publications

(10 citation statements)

References 26 publications

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“…Harangi et al 54 Adem et al 55 proposed a novel technique for hemorrhage detection based on particle swarm optimization using the firefly algorithm. In the segmentation process, blood vessels were separated from the area of the hemorrhage and detected by using Gabor and top-hat transformation.…”

Section: Blood Vessel Segmentation and Extraction Methodsmentioning

confidence: 99%

Detection of diabetic retinopathy and related retinal disorders using fundus images based on deep learning and image processing techniques: A comprehensive review

Lalithadevi

Krishnaveni²

2022

Concurrency and Computation

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Diabetes mellitus is a chronic disorder disease in which a person's body fails to adhere insulin produced by their pancreas or unable to segregate enough insulin due to harmonic imbalance. Diabetic people are suffering from eye disorders like diabetic retinopathy (DR), glaucoma and various diseases such as neuropathy, nephropathy, cardiomyopathy over long intervals. One of the most prevalent diabetic consequence is DR. Detecting the morphological variations in retina is difficult and requires an effective automated detection system. DR can be predicted in earlier stage using tremendous development of deep learning models and image processing techniques. Recently, many research articles have been published in DR diagnosis system. This article shows a comprehensive review of automated diagnostic methods for DR detection and other related eye disorders from several points: Causes for DR, publicly available datasets, image preprocessing, segmentation of various DR lesions, feature optimization, various deep learning models, and open research challenges. The study offers a thorough overview of DR detection techniques, which delivers valuable information for researchers, medical professionals, and DR affected patients.

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Section: Blood Vessel Segmentation and Extraction Methodsmentioning

confidence: 99%

Detection of diabetic retinopathy and related retinal disorders using fundus images based on deep learning and image processing techniques: A comprehensive review

Lalithadevi

Krishnaveni²

2022

Concurrency and Computation

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“…They obtained an area under the ROC curve (AUC) of 0.87 at the image level and 0.96 at the splat level. Adem et al [8] suggested an iterative thresholding method for the detection of hemorrhages based on the Firefly Algorithm (FFA) and Particle Swarm Optimization Algorithm (PSOA), in which the number of hemorrhage areas and the number of pixels in these areas are determined. Gabor and Top-hat transformations are utilized for vessel segmentation to remove blood vessels from an image.…”

Section: Literature Reviewmentioning

confidence: 99%

Hemorrhage Segmentation in Mobile-Phone Retinal Images Using Multi-Region Contrast Enhancement and Iterative Nick Thresholding Region Growing

Chandhakanond

Aimmanee

2022

Preprint

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Hemorrhages segmentation in retinal images is challenging because the sizes and shapes vary for each hemorrhage, the intensity is close to the blood vessels and macula, and the intensity within each hemorrhage is often non-uniform for the large ones. Hemorrhage segmentation in mobile-phone retinal images is even more challenging because mobile-phone retinal images usually have poorer contrast, more places of shadows, and uneven illumination compared to those obtained from the table-top ophthalmoscope. In this work, the proposed KMMRC-INRG method enhances the performance of the segmentation of the hemorrhages with the nonuniform intensity in poor lighting conditions on mobile-phone images. It improves the uneven illumination of mobile-phone retinal images using a proposed method, K-Mean Multi-Region Contrast Enhancement (KMMRC). It also enhances the segmentation of the boundary of the hemorrhage blobs using a novel Iterative Nick Thresholding Region Growing (INRG) method before applying an SVM classifier based on hue, saturation, and brightness features. We find this approach can achieve as high as 80.18%, 91.26%, and 85.36% for recall, precision, and F1-measure, respectively. The F1-measure score improves up to 19.02% compared to a state-of-the-art method DT-HSVE tested on the same full dataset and as much as 58.88% when considering only images with large-size hemorrhages.

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“…Each particle position is adjusted to the best position by taking advantage of previous position experiences. This process is repeated until reaching a termination criterion, such as the maximum iteration number used in this study [27,28].…”

Section: Pso-based Iterative Thresholding Approachmentioning

confidence: 99%

A hybrid model based on the convolutional neural network model and artificial bee colony or particle swarm optimization-based iterative thresholding for the detection of bruised apples

Hekim¹,

Cömert²,

Adem³

2020

Turk J Elec Eng & Comp Sci

Self Cite

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In this study, apple images taken with near-infrared (NIR) cameras were classified as bruised and healthy objects using iterative thresholding approaches based on artificial bee colony (ABC) and particle swarm optimization (PSO) algorithms supported by a convolutional neural network (CNN) deep learning model. The proposed model includes the following stages: image acquisition, image preprocessing, the segmentation of anatomical regions (stemcalyx regions) to be discarded, the detection of bruised areas on the apple images, and their classification. For this aim, by using the image acquisition platform with a NIR camera, a total of 1200 images at 6 different angles were taken from 200 apples, of which 100 were bruised and 100 healthy. In order to increase the success of detection and classification, adaptive histogram equalization (AHE), edge detection, and morphological operations were applied to the images in the preprocessing stage, respectively. First, in order to segment and discard the stem-calyx anatomical regions of the images, the CNN model was trained by using the preprocessed images. Second, the threshold value was determined by means of the ABC/PSO-based iterative thresholding approach on the images whose stem-calyx regions were discarded, and then the bruised areas on the images with no stem-calyx anatomical regions were detected by using the determined threshold value. Finally, the apple images were classified as bruised and healthy objects by using this threshold value. In order to illustrate the classification success of our approaches, the same classification experiments were reimplemented by directly using the CNN model alone on the preprocessed images with no ABC and PSO approaches. Experimental results showed that the hybrid model proposed in this paper was more successful than the CNN model in which ABCand PSO-based iterative threshold approaches were not used.

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

Cited by 27 publications

References 26 publications

Detection of diabetic retinopathy and related retinal disorders using fundus images based on deep learning and image processing techniques: A comprehensive review

Detection of diabetic retinopathy and related retinal disorders using fundus images based on deep learning and image processing techniques: A comprehensive review

Hemorrhage Segmentation in Mobile-Phone Retinal Images Using Multi-Region Contrast Enhancement and Iterative Nick Thresholding Region Growing

A hybrid model based on the convolutional neural network model and artificial bee colony or particle swarm optimization-based iterative thresholding for the detection of bruised apples

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