Radon transform technique for linear structures detection: Application to vessel detection in fluorescein angiography fundus images

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

doi:10.1109/nssmic.2011.6152552

Cited by 17 publications

(22 citation statements)

References 16 publications

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“…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

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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.

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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

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“…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

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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

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“…Fusion rule = max (I1, I2) (11) Then the maximum magnitude intensity components from maximum fusion scheme are reconstructed as a intensity component by inverse Radon transform using equation (10). The new Intensity component is combined with the Hue and Saturation components of the multispectral image by Backward IHS transform using equation (4) so that the high resolution multispectral image (HRMI) is obtained.…”

Section: Image Fusion Methodsmentioning

confidence: 99%

“…The source can either be a single point or it can be an array of sources [9]. Radon transform is used to extract the suitable and important information from the images [10]. Radon transform detect the image coefficients through rotation, scaling and other operations quickly and efficiently [11].…”

Section: Introductionmentioning

confidence: 99%

Hybrid fusion approach of panchromatic and multispectral images using IHS and radon transform

Sujitha

Selvathi²,

Azees

2013

2013 Ieee Conference on Information and Communication Technologies

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In remote sensing applications there is a need of an image with high spatial resolution and high spectral resolution. To obtain this kind of image the panchromatic image is fused with the multispectral image using hybrid transform. The basic ideas behind this method is that the extraction of the spectral information from the multispectral image and fuse this information with the new intensity component obtained from the radon transform of panchromatic and multispectral image. The fusion used in this method is maximum coefficient scheme, which takes the largest magnitude while comparing. In this fusion method, higher value Radon transformed coefficients of intensity component are injected. To this, hue and saturation components are added. So High Resolution Multispectral Image (HRMI) is obtained with high spectral and spatial quality which is better than the image obtained from IHS method.

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Radon transform technique for linear structures detection: Application to vessel detection in fluorescein angiography fundus images

Cited by 17 publications

References 16 publications

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

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

Hybrid fusion approach of panchromatic and multispectral images using IHS and radon transform

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