Automatic Optic Disc Detection in Color Retinal Images by Local Feature Spectrum Analysis

Zhou, Wei; Wu, Hao; Wu, Chengdong; Yu, Xiaoqing; Yi, Yugen

doi:10.1155/2018/1942582

Cited by 10 publications

(5 citation statements)

References 34 publications

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“…Optic disc localization and removal from the retinal image is necessary for the detection of DR features. Different optic disc localization algorithms such as local feature spectrum analysis [25] , entropy calculation [26] , bat meta-heuristic algorithm [27] , statistical edge detection and circular Hough transformation [28] , structured learning [29] etc. have been suggested before.…”

Section: Methodsmentioning

confidence: 99%

A hybrid approach for diagnosing diabetic retinopathy from fundus image exploiting deep features

Mahmood,

Aktar,

Kader

2023

Heliyon

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Section: Methodsmentioning

confidence: 99%

A hybrid approach for diagnosing diabetic retinopathy from fundus image exploiting deep features

Mahmood,

Aktar,

Kader

2023

Heliyon

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“…Usually, with the usage of the extracted features, a classification model can be trained which identifies the normal class versus abnormal class. Many classifiers have been employed to distinguish the two classes based on the extracted features, for instance, artificial neural network (ANN) [34], K-nearest neighbor (KNN) [32], support vector machine (SVM) [35], least square support vector machine 2 Wireless Communications and Mobile Computing (LS-SVM) [29], and extreme learning machine (ELM) [36].…”

Section: Machine Learning-(ml-) Basedmentioning

confidence: 99%

Unsupervised Anomaly Detection for Glaucoma Diagnosis

Zhou

Yuan²,

et al. 2021

Wireless Communications and Mobile Computing

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With the rapid development of high tech, Internet of Things (IoT) and artificial intelligence (AI) achieve a series of achievements in the healthcare industry. Among them, automatic glaucoma diagnosis is one of them. Glaucoma is second leading cause of blindness in the world. Although many automatic glaucoma diagnosis approaches have been proposed, they still face the following two challenges. First, the data acquisition of diseased images is extremely expensive, especially for disease with low occurrence, leading to the class imbalance. Second, large-scale labeled data are hard to obtain in medical image domain. The aforementioned challenges limit the practical application of these approaches in glaucoma diagnosis. To address these disadvantages, this paper proposes an unsupervised anomaly detection framework based on sparse principal component analysis (SPCA) for glaucoma diagnosis. In the proposed approach, we just employ the one-class normal (nonglaucoma) images for training, so the class imbalance problem can be avoided. Then, to distinguish the glaucoma (abnormal) images from the normal images, a feature set consisting of segmentation-based features and image-based features is extracted, which can capture the shape and textural changes. Next, SPCA is adopted to select the effective features from the feature set. Finally, with the usage of the extracted effective features, glaucoma diagnosis can be automatically accomplished via introducing the T 2 statistic and the control limit, overcoming the issue of insufficient labeled samples. Extensive experiments are carried out on the two public databases, and the experimental results verify the effectiveness of the proposed approach.

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“…Optic disc [3,4] is the bright yellow part of the retina containing more neurons. The shape slightly looks like circular, but it varies from person to person.…”

Section: Retinal Fundus Imagesmentioning

confidence: 99%

A Review on the optic disc and optic cup segmentation and classification approaches over retinal fundus images for detection of glaucoma

Veena¹,

Muruganandham

Kumaran³

2020

SN Appl. Sci.

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Glaucoma is one of the leading severe retinal disease which damages the optic nerve head on the retinal part of the eye irreversibly. Once the person is diagnosed with glaucoma, it cannot be treated entirely, but it can be controlled. If glaucoma is not diagnosed in time, it will lead to vision loss by damaging the Optic Nerve Head. The glaucoma detection is performed based on the optic disc and optic cup parameters on the retinal part of the eye. In the existing system, many image processing and machine learning techniques used for the segmentation and classification of optic disc and optic cup. To improve the precision of diagnosis, the existing techniques used need an improvement. This article helps readers with more information about the existing methods applied for the diagnosis of glaucoma, it also lists the research gaps and technical challenges to improve the accuracy of segmentation and classification methods.

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Automatic Optic Disc Detection in Color Retinal Images by Local Feature Spectrum Analysis

Cited by 10 publications

References 34 publications

A hybrid approach for diagnosing diabetic retinopathy from fundus image exploiting deep features

A hybrid approach for diagnosing diabetic retinopathy from fundus image exploiting deep features

Unsupervised Anomaly Detection for Glaucoma Diagnosis

A Review on the optic disc and optic cup segmentation and classification approaches over retinal fundus images for detection of glaucoma

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