- 标题
- 摘要
- 关键词
- 实验方案
- 产品
-
Automated Method for Retinal Artery/Vein Separation via Graph Search Metaheuristic Approach
摘要： Separation of the vascular tree into arteries and veins is a fundamental prerequisite in the automatic diagnosis of retinal biomarkers associated with systemic and neurodegenerative diseases. In this paper, we present a novel graph search metaheuristic approach for automatic separation of arteries/veins (A/V) from color fundus images. Our method exploits local information to disentangle the complex vascular tree into multiple subtrees, and global information to label these vessel subtrees into arteries and veins. Given a binary vessel map, a graph representation of the vascular network is constructed representing the topological and spatial connectivity of the vascular structures. Based on the anatomical uniqueness at vessel crossing and branching points, the vascular tree is split into multiple subtrees containing arteries and veins. Finally, the identified vessel subtrees are labeled with A/V based on a set of hand-crafted features trained with random forest classifier. The proposed method has been tested on four different publicly available retinal datasets with an average accuracy of 94.7%, 93.2%, 96.8% and 90.2% across AV-DRIVE, CT-DRIVE, INSPIRE-AVR and WIDE datasets, respectively. These results demonstrate the superiority of our proposed approach in outperforming state-of-the-art methods for A/V separation.
关键词： Graph search,Vessel keypoints,Artery/Vein classification,Retinal image
更新于2025-09-23 15:23:52
-
[IEEE 2018 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET) - Chennai (2018.3.22-2018.3.24)] 2018 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET) - Automatic Segmentation of Exudates in Retinal Images
摘要： This paper presents a new technique for segmentation of exudates in fundus images. This technique is based on Discrete Wavelet Transform (DWT) and histogram based thresholding procedure. In this work, Optic Disc (OD) is eliminated using DWT from original green component image prior segmentation of exudates. This step aids to avoid the misclassification of exudates region. Histogram based threshold calculation procedure is introduced for segmentation of bright regions in green component image. Hard exudates are obtained after masking the OD region in segmented bright regions of the green component image. This technique was evaluated on images from DIARETDB0 and DIARETDB1 databases. The average sensitivity, specificity and accuracy achieved by proposed method are 0.7890, 0.9972 and 0.9964 respectively. Comparison with existing methods offered in the literature shows that the performance of proposed approach is significant.
关键词： Optic Disc,Retinal image,Segmentation,Exudates,Discrete Wavelet Transform
更新于2025-09-23 15:22:29
-
[IEEE 2019 URSI Asia-Pacific Radio Science Conference (AP-RASC) - New Delhi, India (2019.3.9-2019.3.15)] 2019 URSI Asia-Pacific Radio Science Conference (AP-RASC) - Physics based Device Modeling of GaN High Electron Mobility Transistor (HEMT) for Terahertz Applications
摘要： Scanning laser ophthalmoscopes (SLOs) can be used for early detection of retinal diseases. With the advent of latest screening technology, the advantage of using SLO is its wide field of view, which can image a large part of the retina for better diagnosis of the retinal diseases. On the other hand, during the imaging process, artefacts such as eyelashes and eyelids are also imaged along with the retinal area. This brings a big challenge on how to exclude these artefacts. In this paper, we propose a novel approach to automatically extract out true retinal area from an SLO image based on image processing and machine learning approaches. To reduce the complexity of image processing tasks and provide a convenient primitive image pattern, we have grouped pixels into different regions based on the regional size and compactness, called superpixels. The framework then calculates image based features reflecting textural and structural information and classifies between retinal area and artefacts. The experimental evaluation results have shown good performance with an overall accuracy of 92%.
关键词： retinal artefacts extraction,Feature selection,retinal image analysis,scanning laser ophthalmoscope (SLO)
更新于2025-09-23 15:19:57
-
A fully automated pipeline of extracting biomarkers to quantify vascular changes in retina-related diseases
摘要： This paper presents an automated system for extracting retinal vascular biomarkers for early detection of diabetes. The proposed retinal vessel enhancement, segmentation, optic disc (OD) and fovea detection algorithms provide fundamental tools for extracting the vascular network within the pre-defined region of interest. Based on that, the artery/vein classification, vessel width, tortuosity and fractal dimension measurement tools are used to assess a large number of quantitative vascular biomarkers. We evaluate our pipeline module by module against human annotations. The results indicate that our automated system is robust to the localisation of OD and fovea, segmentation of vessels and classification of arteries/veins. The proposed pipeline helps to increase the effectiveness of the biomarkers extraction and analysis for the early diabetes, and therefore, has the large potential of being further incorporated into a computer-aided diagnosis system.
关键词： diabetes,Retinal image analysis,vessel biomarkers,computer-aided diagnosis
更新于2025-09-19 17:15:36
-
Exudates Detection Using Morphology Mean Shift Algorithm in Retinal Images
摘要： Exudates is a serious complication causing blindness in diabetic retinopathy (DR) patients. The main objective of this study is to develop a novel method to detect exudates lesions in color retinal images by using a morphology mean shift algorithm (MMSA). The proposed methods start with a normalization of the retinal image, contrast enhancement, noise removal, and the localization of the OD. Then, a coarse segmentation method by using mean shift provides a set of exudates and non-exudates candidates. Finally, a classification using the mathematical morphology algorithm (MMA) procedure is applied, in order to keep only exudates pixels. The optimal value parameters of the MMA will facilitate an increase of the accuracy results from solely MSA method by 13.10%. Based on a comparison between the results and ground truth images, the proposed method obtained an average sensitivity, specificity, and accuracy for of detecting exudates as 98.40%, 98.13%, and 98.35%, respectively.
关键词： retinal image,mathematical morphology,mean shift algorithm,Diabetic retinopathy,exudates
更新于2025-09-19 17:15:36
-
[IEEE 2019 International Topical Meeting on Microwave Photonics (MWP) - Ottawa, ON, Canada (2019.10.7-2019.10.10)] 2019 International Topical Meeting on Microwave Photonics (MWP) - Free Carrier Plasma GeSn Modulator for Mid-Infrared Integrated Microwave Photonics
摘要： Scanning laser ophthalmoscopes (SLOs) can be used for early detection of retinal diseases. With the advent of latest screening technology, the advantage of using SLO is its wide field of view, which can image a large part of the retina for better diagnosis of the retinal diseases. On the other hand, during the imaging process, artefacts such as eyelashes and eyelids are also imaged along with the retinal area. This brings a big challenge on how to exclude these artefacts. In this paper, we propose a novel approach to automatically extract out true retinal area from an SLO image based on image processing and machine learning approaches. To reduce the complexity of image processing tasks and provide a convenient primitive image pattern, we have grouped pixels into different regions based on the regional size and compactness, called superpixels. The framework then calculates image based features reflecting textural and structural information and classifies between retinal area and artefacts. The experimental evaluation results have shown good performance with an overall accuracy of 92%.
关键词： retinal artefacts extraction,Feature selection,retinal image analysis,scanning laser ophthalmoscope (SLO)
更新于2025-09-19 17:13:59
-
Localization and segmentation of optic disc in retinal images using Circular Hough transform and Grow Cut algorithm
摘要： Automated retinal image analysis has been emerging as an important diagnostic tool for early detection of eye-related diseases such as glaucoma and diabetic retinopathy. In this paper, we have presented a robust methodology for optic disc detection and boundary segmentation, which can be seen as the preliminary step in the development of a computer-assisted diagnostic system for glaucoma in retinal images. The proposed method is based on morphological operations, the circular Hough transform and the grow-cut algorithm. The morphological operators are used to enhance the optic disc and remove the retinal vasculature and other pathologies. The optic disc center is approximated using the circular Hough transform, and the grow-cut algorithm is employed to precisely segment the optic disc boundary. The method is quantitatively evaluated on five publicly available retinal image databases DRIVE, DIARETDB1, CHASE_DB1, DRIONS-DB, Messidor and one local Shifa Hospital Database. The method achieves an optic disc detection success rate of 100% for these databases with the exception of 99.09% and 99.25% for the DRIONS-DB, Messidor, and ONHSD databases, respectively. The optic disc boundary detection achieved an average spatial overlap of 78.6%, 85.12%, 83.23%, 85.1%, 87.93%, 80.1%, and 86.1%, respectively, for these databases. This unique method has shown significant improvement over existing methods in terms of detection and boundary extraction of the optic disc.
关键词： Optic disc,Image analysis,Glaucoma detection,Retinal image analysis,Growcut algorithm
更新于2025-09-10 09:29:36
-
Development of an Automated Screening System for Retinopathy of Prematurity Using a Deep Neural Network for Wide-angle Retinal Images
摘要： Background: Retinopathy of prematurity (ROP) is one of the main causes of childhood blindness. However, insufficient ophthalmologists are qualified for ROP screening. Objective: To evaluate the performance of a deep neural network (DNN) for automated screening of ROP. Methods: The training and test sets came from 420,365 wide-angle retina images from ROP screening. A transfer learning scheme was designed to train the DNN classifier. First, a pre-processing classifier images. Then, pediatric ophthalmologists labeled each image as either ROP or negative. The labeled training set (8090 positive images and 9711 negative ones) was used to fine-tune three candidate DNN classifiers (AlexNet, VGG-16, and GoogLeNet) with the transfer learning approach. The resultant classifiers were evaluated on a test data set of 1742 samples, and compared with five independent pediatric retinal ophthalmologists. The ROC (receiver operating characteristic) curve, ROC area under the curve (AUC) and P-R (precision-recall) curve on the test data set were analyzed. Accuracy, precision, sensitivity (recall), specificity, F1 score, Youden index, and MCC (Matthews correlation coefficient) were evaluated at different sensitivity cutoffs. The data from the five pediatric ophthalmologists were plotted in the ROC and P-R curves to visualize their performances. Results: VGG-16 achieved the best performance. At the cutoff point that maximized F1 score in the precision-recall curve, the final DNN model achieved 98.8% accuracy, 94.1% sensitivity, 99.3% specificity, and 93.0% precision. This was comparable to the pediatric ophthalmologists (98.8% accuracy, 93.5% sensitivity, 99.5% specificity and 96.7% precision). Conclusion: In the screening of ROP using the evaluation of wide-angel retinal images, DNNs had high accuracy, sensitivity, specificity, and precision, comparable to that of pediatric ophthalmologists.
关键词： image classification,retinopathy of prematurity,transfer learning,deep neural network,wide-angle retinal image,computer-aided diagnosis
更新于2025-09-09 09:28:46
-
[IEEE 2018 International Symposium ELMAR - Zadar, Croatia (2018.9.16-2018.9.19)] 2018 International Symposium ELMAR - Bright Lesions Detection on Retinal Images by Convolutional Neural Network
摘要： This paper is focused on automatic detection and classification of diabetic retinopathy symptoms, more specifically on the bright lesions (soft and hard exudates) as one of the primary signs suitable for diabetic retinopathy screening. We use a convolutional neural network (CNN) for bright lesions detection and evaluate achieved results using criterion based on proper comparison of each lesion with ground truth images scored by the ophthalmologist. As input data we use original and geometrically transformed retinal images from Messidor database divided into smaller blocks. In that way we enlarge the training dataset and increase classification accuracy.
关键词： Soft and hard exudates classification,Evaluation method,Retinal image,CNN,Messidor database
更新于2025-09-09 09:28:46
-
[IEEE 2018 25th IEEE International Conference on Image Processing (ICIP) - Athens, Greece (2018.10.7-2018.10.10)] 2018 25th IEEE International Conference on Image Processing (ICIP) - A Differential-Based Approach for Vessel Type Classification in Retinal Images
摘要： Vessel type classi?cation is a preliminary step in quantifying the severity of various diseases. This paper proposes DBA, a simple yet effective vessel type classi?cation method based on the principle that arteries are brighter than veins at the local scale. The weighted local difference of the red channel intensity of the main trunk of each vessel is compared with that of its two immediately neighbouring vessels, a feature that is highly correlated with vessel-recti?ed oxygen capacity, and in turn, vessel type. Experiments on the publicly-available INSPIRE-AVR and DRIVE datasets obtained average vessel accuracies of 0.9217/0.9071, and average pixel accuracies of 0.9602/0.9634 respectively, with particular effectiveness on images with low contrast, non-uniform illumination and colour variation con?rmed on the SiMES1 dataset.
关键词： retinal image,artery-vein classi?cation
更新于2025-09-09 09:28:46