In this work,we aim to introduce some modifications to the Anam-Net deep neural network(DNN)model for segmenting optic cup(OC)and optic disc(OD)in retinal fundus images to estimate the cup-to-disc ratio(CDR).The CDR i...In this work,we aim to introduce some modifications to the Anam-Net deep neural network(DNN)model for segmenting optic cup(OC)and optic disc(OD)in retinal fundus images to estimate the cup-to-disc ratio(CDR).The CDR is a reliable measure for the early diagnosis of Glaucoma.In this study,we developed a lightweight DNN model for OC and OD segmentation in retinal fundus images.Our DNN model is based on modifications to Anam-Net,incorporating an anamorphic depth embedding block.To reduce computational complexity,we employ a fixed filter size for all convolution layers in the encoder and decoder stages as the network deepens.This modification significantly reduces the number of trainable parameters,making the model lightweight and suitable for resource-constrained applications.We evaluate the performance of the developed model using two publicly available retinal image databases,namely RIM-ONE and Drishti-GS.The results demonstrate promising OC segmentation performance across most standard evaluation metrics while achieving analogous results for OD segmentation.We used two retinal fundus image databases named RIM-ONE and Drishti-GS that contained 159 images and 101 retinal images,respectively.For OD segmentation using the RIM-ONE we obtain an f1-score(F1),Jaccard coefficient(JC),and overlapping error(OE)of 0.950,0.9219,and 0.0781,respectively.Similarly,for OC segmentation using the same databases,we achieve scores of 0.8481(F1),0.7428(JC),and 0.2572(OE).Based on these experimental results and the significantly lower number of trainable parameters,we conclude that the developed model is highly suitable for the early diagnosis of glaucoma by accurately estimating the CDR.展开更多
Glaucoma as an irreversible blinding opioid neuropathy disease, its blindness rate is the second only after cataract in the world. The optic cup-to-disc ratio(CDR) is generally considered to be an important clinical i...Glaucoma as an irreversible blinding opioid neuropathy disease, its blindness rate is the second only after cataract in the world. The optic cup-to-disc ratio(CDR) is generally considered to be an important clinical indicator for judging the severity of glaucoma by ophthalmologists from retinal fundus image. In this letter, we propose an automatic CDR measurement method that consists of a novel optic disc localization method and a simultaneous optic disc and cup segmentation network based on the improved U shape deep convolutional neural network. Experimental results demonstrate that the proposed method can achieve superior performance when compared with other existing methods. Thus, our method can be used as a powerful tool for glaucoma-assisted diagnosis.展开更多
Objective:To explore the relationship between the polymorphism of optic disc related genes and the susceptibility to primary open angle glaucoma(POAG)in Inner Mongolia.Methods:A retrospective study was adopted to incl...Objective:To explore the relationship between the polymorphism of optic disc related genes and the susceptibility to primary open angle glaucoma(POAG)in Inner Mongolia.Methods:A retrospective study was adopted to include 108 patients who were diagnosed as POAG in six hospitals in Hohhot and Baotou from January of 2014 to December of 2016(POAG group).At the same time,120 healthy examinees were included in the control group.1-2 ml of whole blood was collected by use of EDTA anticoagulation tubes from each patient in these two groups.It was required to fully mix EDTA with whole blood in order to extract genomic DNA and place it in the-20℃ refrigerator.Mass spectrometry was used to identify the genotype of single nucleotide polymorphism(SNP)of RFTN1(rs690037),ATOH7(rs7916697,rs3858145),CDC7(rs1192415),CDKN2B(rs1063192)and SIX(rs10483727)in 108 POAG patients and 120 normal controls.χ^(2) test and binary logistic regression were used to analyze the relationship between the genetic polymorphism and the occurrence of POAG.Results:The frequency of G allele at CDKN2B(rs1063192)in POAG group was significantly higher than that in the control group(27%vs.17%),and the difference was of statistical significance[odds ratio(OR)=1.824,95%confidence interval(CI):1.163-2.861,p=.008];As to the comparison in the frequency of allele at the other 7 SNPs between the two groups,the differences were statistically significant(all p>.05).Additive and dominant models at rs1063192 indicated that the individuals with G allele were more susceptible to POAG,and the difference was of statistical significance(p<.05),and recessive models showed that the risk in the individuals with A allele was not significantly reduced,and the difference was of no statistical significance(p>.05).There was no statistically significant difference in the distribution of genotypes at other SNPs between POAG group and the control group(p>.05).Conclusions:The genetic polymorphism of CDKN2B(rs1063192)is associated with the susceptibility to POAG,and G allele may increase the risk for POAG.展开更多
Optic disc(OD)detection is a main step while developing automated screening systems for diabetic retinopathy.We present a method to automatically locate and extract the OD in digital retinal fundus images.Based on the...Optic disc(OD)detection is a main step while developing automated screening systems for diabetic retinopathy.We present a method to automatically locate and extract the OD in digital retinal fundus images.Based on the property that main blood vessels gather in OD,the method starts with Otsu thresholding segmentation to obtain candidate regions of OD.Consequently,the main blood vessels which are segmented in H channel of color fundus images in Hue saturation value(HSV)space.Finally,a weighted vessels’direction matched filter is proposed to roughly match the direction of the main blood vessels to get the OD center which is used to pick the true OD out from the candidate regions of OD.The proposed method was evaluated on a dataset containing 100 fundus images of both normal and diseased retinas and the accuracy reaches 98%.Furthermore,the average time cost in processing an image is 1.3 s.Results suggest that the approach is reliable,and can efficiently detect OD from fundus images.展开更多
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,inte...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 DiaretDB1(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.展开更多
视杯、视盘的区域信息对青光眼的诊断具有重要意义。为提高视网膜图像中杯盘分割的准确性,提出DMSwin-Unet模型。它以Swin-Unet为主干,融合MMAS(Multi-scale Mixed Aggregation and Selection)机制提高瓶颈层的感受野,增强边界与细节特...视杯、视盘的区域信息对青光眼的诊断具有重要意义。为提高视网膜图像中杯盘分割的准确性,提出DMSwin-Unet模型。它以Swin-Unet为主干,融合MMAS(Multi-scale Mixed Aggregation and Selection)机制提高瓶颈层的感受野,增强边界与细节特征的捕获能力;同时通过DCA(Dual Cross-Attention)模块加强跳跃连接中的语义信息交互,提升上下文建模能力。此外,结合杯盘边界模糊、区域不平滑的特点设计了混合损失函数,进一步优化分割边界。在REFUGE、ORIGA、Drishti-GS数据集上,DMSwin-Unet分别取得了视杯Dice分数:89.06%、91.28%、93.35%;视盘Dice分数:96.46%、98.06%、97.85%。实验结果表明,该模型在视杯与视盘分割任务中均优于现有方法,具备良好的临床应用潜力。展开更多
Diabetic Retinopathy(DR)is an eye disease that mainly affects people with diabetes.People affected by DR start losing their vision from an early stage even though the symptoms are identified only at the later stage.On...Diabetic Retinopathy(DR)is an eye disease that mainly affects people with diabetes.People affected by DR start losing their vision from an early stage even though the symptoms are identified only at the later stage.Once the vision is lost,it cannot be regained but can be prevented from causing any further damage.Early diagnosis of DR is required for preventing vision loss,for which a trained ophthalmologist is required.The clinical practice is time-consuming and is not much successful in identifying DR at early stages.Hence,Computer-Aided Diagnosis(CAD)system is a suitable alternative for screening and grading of DR for a larger population.This paper addresses the different stages in CAD system and the challenges in identifying and grading of DR by analyzing various recently evolved techniques.The performance metrics used to evaluate the Computer-Aided Diagnosis system for clinical practice is also discussed.展开更多
基金funded byResearchers Supporting Project Number(RSPD2024R 553),King Saud University,Riyadh,Saudi Arabia.
文摘In this work,we aim to introduce some modifications to the Anam-Net deep neural network(DNN)model for segmenting optic cup(OC)and optic disc(OD)in retinal fundus images to estimate the cup-to-disc ratio(CDR).The CDR is a reliable measure for the early diagnosis of Glaucoma.In this study,we developed a lightweight DNN model for OC and OD segmentation in retinal fundus images.Our DNN model is based on modifications to Anam-Net,incorporating an anamorphic depth embedding block.To reduce computational complexity,we employ a fixed filter size for all convolution layers in the encoder and decoder stages as the network deepens.This modification significantly reduces the number of trainable parameters,making the model lightweight and suitable for resource-constrained applications.We evaluate the performance of the developed model using two publicly available retinal image databases,namely RIM-ONE and Drishti-GS.The results demonstrate promising OC segmentation performance across most standard evaluation metrics while achieving analogous results for OD segmentation.We used two retinal fundus image databases named RIM-ONE and Drishti-GS that contained 159 images and 101 retinal images,respectively.For OD segmentation using the RIM-ONE we obtain an f1-score(F1),Jaccard coefficient(JC),and overlapping error(OE)of 0.950,0.9219,and 0.0781,respectively.Similarly,for OC segmentation using the same databases,we achieve scores of 0.8481(F1),0.7428(JC),and 0.2572(OE).Based on these experimental results and the significantly lower number of trainable parameters,we conclude that the developed model is highly suitable for the early diagnosis of glaucoma by accurately estimating the CDR.
基金supported by the National Natural Science Foundation of China(Nos.61502537 and 61573380)the Hunan Provincial Natural Science Foundation of China(Nos.2018JJ3681 and 2016JJ2150)+3 种基金the Open Project Fund of Key Lab of Digital Signal and Image Processing of Guangdong Province(No.2018GDDSIPL-01)the Mutual Creation Project for Teachers and Students(No.2018gczd022)the 111 Project(No.B18059)the Fundamental Research Funds for the Central Universities of Central South University(No.2018zzts576)
文摘Glaucoma as an irreversible blinding opioid neuropathy disease, its blindness rate is the second only after cataract in the world. The optic cup-to-disc ratio(CDR) is generally considered to be an important clinical indicator for judging the severity of glaucoma by ophthalmologists from retinal fundus image. In this letter, we propose an automatic CDR measurement method that consists of a novel optic disc localization method and a simultaneous optic disc and cup segmentation network based on the improved U shape deep convolutional neural network. Experimental results demonstrate that the proposed method can achieve superior performance when compared with other existing methods. Thus, our method can be used as a powerful tool for glaucoma-assisted diagnosis.
文摘Objective:To explore the relationship between the polymorphism of optic disc related genes and the susceptibility to primary open angle glaucoma(POAG)in Inner Mongolia.Methods:A retrospective study was adopted to include 108 patients who were diagnosed as POAG in six hospitals in Hohhot and Baotou from January of 2014 to December of 2016(POAG group).At the same time,120 healthy examinees were included in the control group.1-2 ml of whole blood was collected by use of EDTA anticoagulation tubes from each patient in these two groups.It was required to fully mix EDTA with whole blood in order to extract genomic DNA and place it in the-20℃ refrigerator.Mass spectrometry was used to identify the genotype of single nucleotide polymorphism(SNP)of RFTN1(rs690037),ATOH7(rs7916697,rs3858145),CDC7(rs1192415),CDKN2B(rs1063192)and SIX(rs10483727)in 108 POAG patients and 120 normal controls.χ^(2) test and binary logistic regression were used to analyze the relationship between the genetic polymorphism and the occurrence of POAG.Results:The frequency of G allele at CDKN2B(rs1063192)in POAG group was significantly higher than that in the control group(27%vs.17%),and the difference was of statistical significance[odds ratio(OR)=1.824,95%confidence interval(CI):1.163-2.861,p=.008];As to the comparison in the frequency of allele at the other 7 SNPs between the two groups,the differences were statistically significant(all p>.05).Additive and dominant models at rs1063192 indicated that the individuals with G allele were more susceptible to POAG,and the difference was of statistical significance(p<.05),and recessive models showed that the risk in the individuals with A allele was not significantly reduced,and the difference was of no statistical significance(p>.05).There was no statistically significant difference in the distribution of genotypes at other SNPs between POAG group and the control group(p>.05).Conclusions:The genetic polymorphism of CDKN2B(rs1063192)is associated with the susceptibility to POAG,and G allele may increase the risk for POAG.
基金National High Technology Research and Development Program of China(863 Program)(No.2006AA020804)Fundamental Research Funds for the Central Universities,China(No.NJ20120007)+2 种基金Jiangsu Province Science and Technology Support Plan,China(No.BE2010652)Program Sponsored for Scientific Innovation Research of College Graduate in Jangsu Province,China(No.CXLX11_0218)Shanghai University Scientific Selection and Cultivation for Outstanding Young Teachers in Special Fund,China(No.ZZGCD15081)。
文摘Optic disc(OD)detection is a main step while developing automated screening systems for diabetic retinopathy.We present a method to automatically locate and extract the OD in digital retinal fundus images.Based on the property that main blood vessels gather in OD,the method starts with Otsu thresholding segmentation to obtain candidate regions of OD.Consequently,the main blood vessels which are segmented in H channel of color fundus images in Hue saturation value(HSV)space.Finally,a weighted vessels’direction matched filter is proposed to roughly match the direction of the main blood vessels to get the OD center which is used to pick the true OD out from the candidate regions of OD.The proposed method was evaluated on a dataset containing 100 fundus images of both normal and diseased retinas and the accuracy reaches 98%.Furthermore,the average time cost in processing an image is 1.3 s.Results suggest that the approach is reliable,and can efficiently detect OD from fundus images.
文摘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 DiaretDB1(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.
文摘Diabetic Retinopathy(DR)is an eye disease that mainly affects people with diabetes.People affected by DR start losing their vision from an early stage even though the symptoms are identified only at the later stage.Once the vision is lost,it cannot be regained but can be prevented from causing any further damage.Early diagnosis of DR is required for preventing vision loss,for which a trained ophthalmologist is required.The clinical practice is time-consuming and is not much successful in identifying DR at early stages.Hence,Computer-Aided Diagnosis(CAD)system is a suitable alternative for screening and grading of DR for a larger population.This paper addresses the different stages in CAD system and the challenges in identifying and grading of DR by analyzing various recently evolved techniques.The performance metrics used to evaluate the Computer-Aided Diagnosis system for clinical practice is also discussed.
