Automated Optic Nerve Head Detection Based on Different Retinal Vasculature Segmentation Methods and Mathematical Morphology

Tavakoli, Meysam; Nazar, Mahdieh; Golestaneh, Alireza; Kalantari, Faraz

doi:10.1109/nssmic.2017.8532764

Cited by 6 publications

(11 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…53 It could also be possible to screen more people and more often with the help of an automatic screening system, since it would be more inexpensive than screening by humans. 2,13,54 Although the final purpose of this study was early detection of DR, we were focused only on detection of MAs. The goal of this work was to develop an automated detection of DR.…”

Section: Resultsmentioning

confidence: 99%

“…Here, we used three different approaches for vessel segmentation. 2 The results of segmentation have been shown in Fig. 3.…”

Section: Vessel Segmentation and Maskingmentioning

confidence: 99%

“…We can compute the magnitude and orientation of the gradient for each pixel based two filtered images. 2,13 In this paper, the Matched filter response to the detection of blood vessels is increased by proposing better filter parameters. 47 The matched filter was first proposed in to detect vessels in retinal images.…”

Section: Vessel Segmentation and Maskingmentioning

confidence: 99%

See 2 more Smart Citations

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

Self Cite

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

Section: Resultsmentioning

confidence: 99%

“…Here, we used three different approaches for vessel segmentation. 2 The results of segmentation have been shown in Fig. 3.…”

Section: Vessel Segmentation and Maskingmentioning

confidence: 99%

See 1 more Smart Citation

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

Self Cite

View full text Add to dashboard Cite

show abstract

“…Details of the individual datasets are as follows: 1-The first set (rural database) was named MUMS-DB (Mashhad University Medical Science Database). The MUMS-DB provided 220 retinal images including 200 cases with DR (with varying severity, mild, moderate and severe) and 20 without DR [61], [65]. The images were obtained via a TOPCON (TRC-50EX) retinal camera at 50 degree field of view (FOV) and mostly obtained from the posterior pole view (including ONH and macula) with a resolution of 2896 × 1944 pixels.…”

Section: Experimental Setups a Databasesmentioning

confidence: 99%

Automated Microaneurysms Detection in Retinal Images Using Radon Transform and Supervised Learning: Application to Mass Screening of Diabetic Retinopathy

et al. 2021

Self Cite

View full text Add to dashboard Cite

Detection of red lesions in color retinal images is a critical step to prevent the development of vision loss and blindness associated with diabetic retinopathy (DR). Microaneurysms (MAs) are the most frequently observed and are usually the first lesions to appear as a consequence of DR. Therefore, their detection is necessary for mass screening of DR. However, detecting these lesions is a challenging task because of the low image contrast, and the wide variation of imaging conditions. Recently, the emergence of computer-aided diagnosis systems offers promising approaches to detect these lesions for diagnostic purposes. In this paper we focus on developing unsupervised and supervised techniques to cope intelligently with the MAs detection problem. In the first step, the retinal images are preprocessed to remove background variation in order to achieve a high level of accuracy in the detection. In the main processing step, important landmarks such as the optic nerve head and retinal vessels are detected and masked using the Radon transform (RT) and multi-overlapping windows. Finally, the MAs are detected and numbered by using a combination of RT and a supervised support vector machine classifier. The method was tested on three publicly available datasets and a local database comprising a total of 749 images. Detection performance was evaluated using sensitivity, specificity, and FROC analysis. From the image analysis viewpoint, DR was detected with a sensitivity of 100% and a specificity of 93% on average across all of these databases. Moreover, from lesion-based analysis the proposed approach detected the MAs with sensitivity of 95.7% with an average of 7 false positives per image. These results compare favourably with the best of the published results to date.

show abstract

“…1,9 A critical feature to recognize DR is to detect microaneurysms (MAs) in the automated screening of DR. 10 MAs are small outpouchings in capillary vessels. 11 They are the first signs of the presence of DR. 12 In this study we present an effective MA detector based on the combination of preprocessing method, Laplacian-of-Gaussian (LoG) edge detector, 1,13 and concept of deep learning. 14 Here, the goal is to compare results of MAs detection with and without vessel segmentation either in normal fundus images or in presence of retinal lesion like in DR. Before MAs detection in the retinal image, the image has to be preprocessed to ensure adequate level of success in detection.…”

Section: Introductionmentioning

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

Self Cite

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

Automated Optic Nerve Head Detection Based on Different Retinal Vasculature Segmentation Methods and Mathematical Morphology

Cited by 6 publications

References 35 publications

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

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

Automated Microaneurysms Detection in Retinal Images Using Radon Transform and Supervised Learning: Application to Mass Screening of Diabetic Retinopathy

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

Contact Info

Product

Resources

About