Optic Disc and Cup Segmentation in Retinal Images for Glaucoma Diagnosis by Locally Statistical Active Contour Model with Structure Prior

Zhou, Wei; Yi, Yugen; Yuan, Guandou; Dai, Jiangyan

doi:10.1155/2019/8973287

Cited by 29 publications

(14 citation statements)

References 44 publications

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“…First, use Dice similarity to measure the similarity of the two sets of segmentation results and the gold standard, and then use Jaccard similarity, which is another commonly used standard for evaluating segmentation accuracy [20].…”

Section: Image Segmentation Experimentsmentioning

confidence: 99%

Retracted: Active Contour Image Segmentation Method for Training Talents of Computer Graphics and Image Processing Technology

Wang

2021

IEEE Access

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Image segmentation is a key technology in the field of image processing and computer image processing. Among them, segmentation methods based on active contour models have developed rapidly in recent years due to their effective processing of complex images such as medical images, and have achieved significant results in medical, military, and industrial fields. This article mainly introduces the training of computer graphics and image processing technology and the method of active contour image segmentation. This article focuses on the study of image segmentation methods, and focuses on the segmentation methods based on active contour models. Firstly, it summarizes two types of segmentation methods based on edge and region, and summarizes their advantages and disadvantages. Then, the segmentation method based on active contour model is studied, and several typical active contour models are comprehensively compared. Finally, the local binary fitting model and the local Gaussian distribution fitting energy model were improved and simulated. And from the development of computer graphics and image processing technology to analyze some methods and means of training this professional talent. The experimental results of this article show that the active contour image segmentation algorithm can not only ensure the image segmentation algorithm, but also reduce the number of iterations and shorten the image segmentation time. Compared with the CV, LBF, and LGIF models introduced in this article, its computational efficiency is increased by 9.2 times, 2.64 Times and 1.44 times.

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Section: Image Segmentation Experimentsmentioning

confidence: 99%

Retracted: Active Contour Image Segmentation Method for Training Talents of Computer Graphics and Image Processing Technology

Wang

2021

IEEE Access

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“…For OD, the acquired dice is 95% and for OC the dice is 85%. Zhou et al presented a locally statistical active contour model with the structure prior (LSACM-SP) approach for joint segmentation [52]. They achieved 95.5% and 84.7% dice values for disc and cup on Drishti and 85.3% and 78.5% values on rim-one.…”

Section: B Comparison With State-of-the-artmentioning

confidence: 99%

“…Figure 6 represents the AUC curves for both Drishti and Rim-one datasets. We obtain an average (AUC) [35] Regression based method 85 ---95 ---Zhou et al [52] Locally statistical active contour model 84.7 ---95.5 ---Son et al [53] Generative Table 9. A high AUC value indicates that the percentage of correctly classified images of the overall system is high.…”

Section: B Comparison With State-of-the-artmentioning

confidence: 99%

CDED-Net: Joint Segmentation of Optic Disc and Optic Cup for Glaucoma Screening

et al. 2020

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Glaucoma is an eye disease that can cause loss of vision by damaging the optic nerve. It is the world's second leading cause of blindness after cataracts. Early diagnosis of glaucoma is a key to prevent permanent blindness as it has no noticeable symptoms in its early stages. Color fundus photography is used for examining the optic disc (OD) which is an important step in the diagnoses of glaucoma. This is done by estimating the cup-to-disc ratio (CDR). In this paper, we proposed a Cup Disc Encoder Decoder Network (CDED-Net) for the joint segmentation of optic disc (OD) and optic cup (OC). We have eradicated the preprocessing and post-processing steps to reduce the computational cost of the overall system. Segmentation of (OD) and OC is modeled as a semantic pixel-wise labeling problem. The model was trained on the DRISHTI-GS, RIM-ONE and REFUGE datasets. Experiments show that our CDED-Net system achieves state-of-the-art OD and OC segmentation results on these datasets.

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“…Based on the extracted ROI around the OD, this paper adopts [11] to segment the OD and OC. In clinical, for a normal fundus image, the rim is the thickest in the inferior (I) sector and thinnest in the temporal (T) sector (as shown in Figure 1).…”

Section: Extraction Of Segmentation-based Featuresmentioning

confidence: 99%

“…With the rapid growth of the worldwide population, the count of eye diseases is also increased. Among them, glaucoma is one of the serious eye diseases that can lead to irreversible vision loss [11]. According to the recent report [12], the number of glaucoma patients is predicted to increase to 80 million by 2020 and to 111.8 million by 2040.…”

Section: Introductionmentioning

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 and Cup Segmentation in Retinal Images for Glaucoma Diagnosis by Locally Statistical Active Contour Model with Structure Prior

Cited by 29 publications

References 44 publications

Retracted: Active Contour Image Segmentation Method for Training Talents of Computer Graphics and Image Processing Technology

Retracted: Active Contour Image Segmentation Method for Training Talents of Computer Graphics and Image Processing Technology

CDED-Net: Joint Segmentation of Optic Disc and Optic Cup for Glaucoma Screening

Unsupervised Anomaly Detection for Glaucoma Diagnosis

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