Fundus Image Segmentation and Feature Extraction for the Detection of Glaucoma: A New Approach

Bokhari, Syedia Tahseen Fatima; Sharif, Muhammad; Yasmin, Mussarat; Fernandes, Steven Lawrence

doi:10.2174/1573405613666170405145913

Cited by 61 publications

(3 citation statements)

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“…Researchers have introduced numerous automated computer-aided diagnosis methods to help physicians. Most of the methods are suggested for malignant disease detection, including brain tumor segmentation and classification [ 15 , 16 ], glaucoma detection [ 17 ], lung cancer detection [ 18 ], and so on. The accurate detection of the infected region, such as ulcers and bleeding, is a most challenging task.…”

Section: Related Workmentioning

confidence: 99%

A Comparative Analysis of Optimization Algorithms for Gastrointestinal Abnormalities Recognition and Classification Based on Ensemble XcepNet23 and ResNet18 Features

Naz

Sharif

et al. 2023

Biomedicines

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Esophagitis, cancerous growths, bleeding, and ulcers are typical symptoms of gastrointestinal disorders, which account for a significant portion of human mortality. For both patients and doctors, traditional diagnostic methods can be exhausting. The major aim of this research is to propose a hybrid method that can accurately diagnose the gastrointestinal tract abnormalities and promote early treatment that will be helpful in reducing the death cases. The major phases of the proposed method are: Dataset Augmentation, Preprocessing, Features Engineering (Features Extraction, Fusion, Optimization), and Classification. Image enhancement is performed using hybrid contrast stretching algorithms. Deep Learning features are extracted through transfer learning from the ResNet18 model and the proposed XcepNet23 model. The obtained deep features are ensembled with the texture features. The ensemble feature vector is optimized using the Binary Dragonfly algorithm (BDA), Moth–Flame Optimization (MFO) algorithm, and Particle Swarm Optimization (PSO) algorithm. In this research, two datasets (Hybrid dataset and Kvasir-V1 dataset) consisting of five and eight classes, respectively, are utilized. Compared to the most recent methods, the accuracy achieved by the proposed method on both datasets was superior. The Q_SVM’s accuracies on the Hybrid dataset, which was 100%, and the Kvasir-V1 dataset, which was 99.24%, were both promising.

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Section: Related Workmentioning

confidence: 99%

A Comparative Analysis of Optimization Algorithms for Gastrointestinal Abnormalities Recognition and Classification Based on Ensemble XcepNet23 and ResNet18 Features

Naz

Sharif

et al. 2023

Biomedicines

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“…Given that in most cases, during segmentation of BV and OD of fundus images, the slight color differences in different retinal anatomic structures, vessel disappearance due to their width variances and the heterogeneous illumination and light reflection in the clinical images cause misinterpretation and misclassification of the image pixels [52], the extraction of relevant image features and appropriate image processing techniques are important for the efficient delineation of the BVs and OD. These are important as they assist physicians to achieve reliable diagnosis as well as proper monitoring and management of patients with sicknesses such as glaucoma and other cardiovascular diseases [53].…”

Section: Clinical Examination Of the Eyementioning

confidence: 99%

Optic Disc and Vessel Segmentation in Retinal Images using Correlation Texture

Fatoki,

Owolawi,

Mapayi

2023

iCARTi

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Globally, glaucoma has been recognized as the secondmost common reason for blindness. This affects the anatomy of the eyes, including blood vessels (BVs) and optic disc (OD). State-of-the-art literature has revealed that this disease is the root cause of degeneration in the OD, with some effects on the morphology of the BVs in the retina. These can result in total blindness if left untreated. Previous studies have shown that to effectively diagnose a disease, it is necessary to automatically detect the OD and the BVs with high accuracy in the fundus image. Segmentation of OD and BVs has, however, been challenging, due to the complex and poor segmentation performances. Such performances arise from the intricacies of vascular network width variation, illumination of images, and the structure of retinal images. Examples are retinal image boundaries on ODs and retinal lesions, due to disease. Hence the need for an investigation of the appropriate image feature(s) that will facilitate improvements to OD and BV segmentation performances. Texture is an important indicator used in image analysis for object segmentation. This work proposed the use of image-correlation texture information computational approaches for the segmentation of BVs, using variant window sizes; and OD using grayscale and green channel in the retinal images. This study revealed that the segmentation of BVs using variant window sizes (15x15) and (17x17) achieved high accuracy values of 95.11 per cent and 94.93 per cent; with accuracy values of 94.21 per cent and 93.98 per cent revealed in the DRIVE and STARE databases, respectively. Usually, sensitivity values of 74.84 per cent and 75.94 per cent, with average sensitivity values of 74.58 percent and 78.48 per cent, are achieved BV segmentation on the window sizes (15x15) and (17x17), indicated in DRIVE and STARE databases, respectively. Also, an average specificity value of 97.08 per cent and 96.77 per cent with average specificity values of 95.77 per cent and 95.18 per cent are achieved for BV segmentation on the window sizes (15x15) and (17x17), on DRIVE and STARE databases, respectively. Further, the segmentation of OD using grayscale achieved a slightly greater accuracy rate (98.59%) compared to the accuracy rate (98.36%) realized on green channel. This study also showed that the proposed approaches for BV and OD segmentation in this research achieved better results compared with several existing methods in the literature.

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“…Therefore, an automatic diagnosis system of retinal eye disease detection from OCT images is necessary for early, accurate, and remote diagnosis and treatment. Recently, advancements in computer technology, including machine learning (ML), medical image processing, and deep learning (DL) made it possible to automate many medical diagnostic systems (Amin et al, 2016; Fatima Bokhari et al, 2017; Khan et al, 2016; Saba et al, 2018). A large number of ML and DL algorithms are being applied in radiography, and computer‐aided diagnosis systems (CADs) are becoming available for different diseases with high accuracy (Umer et al, n.d.‐a; Kamran et al, n.d.; Umer et al, n.d.‐b; Umer et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

A deep feature fusion and selection‐based retinal eye disease detection from OCT images

et al. 2023

Self Cite

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Optical coherence tomography (OCT) is one of the principal imaging modalities for retinal eye disease detection and classification. Different retinal eye diseases are the leading cause of blindness that can be overcome by early detection. However, ophthalmologists are currently carrying out retinal eye disease detection manually with the help of OCT images that may be erroneous and subjective. Different methods have been presented to automate the manual retinal eye disease detection process that needs further improvement in detection accuracy. This research proposed an automatic method for retinal eye disease detection and classification from OCT images using fusion and selection techniques. First, the modified-Alexnet and ResNet-50 are utilized for deep feature vector extraction. In the next step, these vectors are fused serially and rectified by the proposed feature selection framework and passed as input to different machine learning classifiers for retinal disease diagnosis.

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Fundus Image Segmentation and Feature Extraction for the Detection of Glaucoma: A New Approach

Cited by 61 publications

References 0 publications

A Comparative Analysis of Optimization Algorithms for Gastrointestinal Abnormalities Recognition and Classification Based on Ensemble XcepNet23 and ResNet18 Features

A Comparative Analysis of Optimization Algorithms for Gastrointestinal Abnormalities Recognition and Classification Based on Ensemble XcepNet23 and ResNet18 Features

Optic Disc and Vessel Segmentation in Retinal Images using Correlation Texture

A deep feature fusion and selection‐based retinal eye disease detection from OCT images

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