Automated optic nerve head detection in fluorescein angiography fundus images

Tavakoli, Meysam; Toosi, Mohammad Hosein Bahreini; Pourreza, Reza; Banaee, Touka; Pourreza, Hamidreza

doi:10.1109/nssmic.2011.6152553

Cited by 11 publications

(6 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Two standard methods are used to assess the effectiveness of the proposed method and to enable comparison with results reported by others: ROC analysis and SSIM analysis. ROC curves illustrate the tradeoff between sensitivity (Se) and specificity (Sp) for a range of thresholds and enable the identification of an optimal value [83]. However, different classification goals might make the selection of one point on the curve more appropriate for one task whilst another point may be more suitable for a different task.…”

Section: Resultsmentioning

confidence: 99%

Unsupervised automated retinal vessel segmentation based on Radon line detector and morphological reconstruction

Tavakoli

Mehdizadeh

Shahri

et al. 2021

IET Image Processing

View full text Add to dashboard Cite

Retinal blood vessel segmentation and analysis is critical for the computer‐aided diagnosis of different diseases such as diabetic retinopathy. This study presents an automated unsupervised method for segmenting the retinal vasculature based on hybrid methods. The algorithm initially applies a preprocessing step using morphological operators to enhance the vessel tree structure against a non‐uniform image background. The main processing applies the Radon transform to overlapping windows, followed by vessel validation, vessel refinement and vessel reconstruction to achieve the final segmentation. The method was tested on three publicly available datasets and a local database comprising a total of 188 images. Segmentation performance was evaluated using three measures: accuracy, receiver operating characteristic (ROC) analysis, and the structural similarity index. ROC analysis resulted in area under curve values of 97.39%, 97.01%, and 97.12%, for the DRIVE, STARE, and CHASE‐DB1, respectively. Also, the results of accuracy were 0.9688, 0.9646, and 0.9475 for the same datasets. Finally, the average values of structural similarity index were computed for all four datasets, with average values of 0.9650 (DRIVE), 0.9641 (STARE), and 0.9625 (CHASE‐DB1). These results compare with the best published results to date, exceeding their performance for several of the datasets; similar performance is found using accuracy.

show abstract

Section: Resultsmentioning

confidence: 99%

Unsupervised automated retinal vessel segmentation based on Radon line detector and morphological reconstruction

Tavakoli

Mehdizadeh

Shahri

et al. 2021

IET Image Processing

View full text Add to dashboard Cite

show abstract

“…The first rural database was named Mashhad University Medical Science Database (MUMS-DB). The MUMS-DB provided 15,49,50 The second dataset was the DRIVE database consisting of 40 images with image resolution of 768 × 584 pixels in which 33 cases did not have any sign of DR and 7 ones showed signs of early or mild DR with a 45 degree FOV. For algorithms that operate in a supervised manner this database is often divided into a testing and training set, each containing 20 images.…”

Section: Methods 21 Databasesmentioning

confidence: 99%

The efficacy of microaneurysms detection with and without vessel segmentation in color retinal images

Tavakoli

Nazar

Mehdizadeh

2020

Medical Imaging 2020: Computer-Aided Diagnosis

View full text Add to dashboard Cite

Computer-Aided Diagnosis systems are required to extract suitable information about retina and its changes. In particular, identifying objects of interest such as lesions and anatomical structures from the retinal images is a challenging and iterative process that is doable by image processing approaches. Microaneurysm (MAs) are one set of these changes that caused by diabetic retinopathy (DR). In fact, MAs detection is the main step for identification of DR in the retinal images analysis. The objective of this study is to apply an automated method for detection of MAs and compare the results of detection with and without vessel segmentation and masking either in the normal or abnormal image. The steps for the detection and segmentation are as follows. At the first step, we did preprocessing, by using top-hat transformation. Our main processing was included applying Radon transform, to segment the vessels and masking them. At last, we did MAs detection step using combination of Laplacian-of-Gaussian and Convolutional Neural Networks. To evaluate the accuracy of our proposed method, we compare the output of our proposed method with the ground truth that collected by ophthalmologists. With vessel segmentation, our algorithm found sensitivity of more than 85% in detection of MAs with 11 false positive rate per image for 100 color images in a local retinal database and 20 images of a public dataset (DRIVE). Also without vessel segmentation, our automated algorithm finds sensitivity of about 90% in detection of MAs with 73 false positives per image for all 120 images of two databases. In conclusion, with vessel segmentation we have acceptable sensitivity and specificity, as a necessary step in some diagnostic algorithm for retinal pathology.

show abstract

“…The images were obtained at 50 degree field of view (FOV) and mostly obtained from the posterior pole view (including ONH and macula) with of resolution 2896 × 1944 pixels. [40][41][42] The second dataset was the DRIVE database consisting of 40 images with image resolution of 768 × 584 pixels in which 33 cases did not have any sign of DR and 7 ones showed signs of early or mild DR with a 45 degree FOV. For algorithms that operate in a supervised manner this database is often divided into a testing and training set, each containing 20 images.…”

Section: Methodsmentioning

confidence: 99%

Comparison different vessel segmentation methods in automated microaneurysms detection in retinal images using convolutional neural networks

Tavakoli¹,

Nazar²

2020

Medical Imaging 2020: Biomedical Applications in Molecular, Structural, and Functional Imaging

View full text Add to dashboard Cite

Image processing techniques provide important assistance to physicians and relieve their workload in different tasks. In particular, identifying objects of interest such as lesions and anatomical structures from the image is a challenging and iterative process that can be done by computerized approaches in a successful manner. Microaneurysms (MAs) detection is a crucial step in retinal image analysis algorithms. The goal of MAs detection is to find the progress and at last identification of diabetic retinopathy (DR) in the retinal images. The objective of this study is to apply three retinal vessel segmentation methods, Laplacian-of-Gaussian (LoG), Canny edge detector, and Matched filter to compare results of MAs detection using combination of unsupervised and supervised learning either in the normal images or in the presence of DR. The steps for the algorithm are as following: 1) Preprocessing and Enhancement, 2) vessel segmentation and masking, 3) MAs detection and Localization using combination of Matching based approach and Convolutional Neural Networks. To evaluate the accuracy of our proposed method, we compared the output of our method with the ground truth that collected by ophthalmologists. By using the LoG vessel segmentation, our algorithm found sensitivity of more than 85% in detection of MAs for 100 color images in a local retinal database and 40 images of a public dataset (DRIVE). For the Canny vessel segmentation, our automated algorithm found sensitivity of more than 80% in detection of MAs for all 140 images of two databases. And lastly, using Matched filter, our algorithm found sensitivity of more than 87% in detection of MAs in both local and DRIVE datasets. arXiv:2005.09097v1 [physics.med-ph] 18 Apr 2020

show abstract

Automated optic nerve head detection in fluorescein angiography fundus images

Cited by 11 publications

References 9 publications

Unsupervised automated retinal vessel segmentation based on Radon line detector and morphological reconstruction

Unsupervised automated retinal vessel segmentation based on Radon line detector and morphological reconstruction

The efficacy of microaneurysms detection with and without vessel segmentation in color retinal images

Comparison different vessel segmentation methods in automated microaneurysms detection in retinal images using convolutional neural networks

Contact Info

Product

Resources

About