Extraction of Capillary Non-perfusion from Fundus Fluorescein Angiogram

Sivaswamy, Jayanthi; Agarwal, Amit; Chawla, Mayank; Rani, Alka; Das, Taraprasad

doi:10.1007/978-3-540-92219-3_13

Cited by 8 publications

(11 citation statements)

References 11 publications

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“…A modified quotient based approach[ 15 ] was used to model the nonuniform illumination as a multiplicative degradation function that can be described by Eq. (10).…”

Section: Methodsmentioning

confidence: 99%

“…I (x, y) is input, I 0 ( x, y ) will be corrected image, l 0 is ideal illumination value and I s ( x, y ) is smoothed image that can be produced from original image using median filter with large window size. [ 15 ]…”

Section: Methodsmentioning

confidence: 99%

“…[ 14 ] Another study, a modified quotient based approach and nonuniform illumination model were applied to correct the nonuniform illumination in fundus fluorescein angiograms. [ 15 ] The optic disk was segmented in retinal images by means of color mathematical morphology and the use of active contours after using an image enhancement technique. Also, as a preprocessing technique, color remapping was performed after single channel enhancing.…”

Section: Introductionmentioning

confidence: 99%

“…Also, we performed CLAHE and polynomial transformation operator for contrast enhancement in retinal images. [ 3 10 13 15 18 19 20 ] We assessed the techniques quantitatively and compare them to choose the most suitable preprocessing techniques for further works.…”

Section: Introductionmentioning

confidence: 99%

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A comparative study on preprocessing techniques in diabetic retinopathy retinal images: Illumination correction and contrast enhancement

et al. 2015

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To investigate the effect of preprocessing techniques including contrast enhancement and illumination correction on retinal image quality, a comparative study was carried out. We studied and implemented a few illumination correction and contrast enhancement techniques on color retinal images to find out the best technique for optimum image enhancement. To compare and choose the best illumination correction technique we analyzed the corrected red and green components of color retinal images statistically and visually. The two contrast enhancement techniques were analyzed using a vessel segmentation algorithm by calculating the sensitivity and specificity. The statistical evaluation of the illumination correction techniques were carried out by calculating the coefficients of variation. The dividing method using the median filter to estimate background illumination showed the lowest Coefficients of variations in the red component. The quotient and homomorphic filtering methods after the dividing method presented good results based on their low Coefficients of variations. The contrast limited adaptive histogram equalization increased the sensitivity of the vessel segmentation algorithm up to 5% in the same amount of accuracy. The contrast limited adaptive histogram equalization technique has a higher sensitivity than the polynomial transformation operator as a contrast enhancement technique for vessel segmentation. Three techniques including the dividing method using the median filter to estimate background, quotient based and homomorphic filtering were found as the effective illumination correction techniques based on a statistical evaluation. Applying the local contrast enhancement technique, such as CLAHE, for fundus images presented good potentials in enhancing the vasculature segmentation.

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“…A modified quotient based approach[ 15 ] was used to model the nonuniform illumination as a multiplicative degradation function that can be described by Eq. (10).…”

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A comparative study on preprocessing techniques in diabetic retinopathy retinal images: Illumination correction and contrast enhancement

et al. 2015

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“…These procedures usually suffer from several drawbacks such as the variable expertise, biases in diagnosis which are also affected by quality of images. 3 , 4 , 6 , 7 …”

Section: Introductionmentioning

confidence: 99%

Detection of retinal capillary nonperfusion in fundus fluorescein angiogram of diabetic retinopathy

Rasta¹,

Nikfarjam²,

Javadzadeh³

2016

Bioimpacts

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Introduction: Retinal capillary nonperfusion (CNP) is one of the retinal vascular diseases in diabetic retinopathy (DR) patients. As there is no comprehensive detection technique to recognize CNP areas, we proposed a different method for computing detection of ischemic retina, non-perfused (NP) regions, in fundus fluorescein angiogram (FFA) images. Methods: Whilst major vessels appear as ridges, non-perfused areas are usually observed as ponds that are surrounded by healthy capillaries in FFA images. A new technique using homomorphic filtering to correct light illumination and detect the ponds surrounded in healthy capillaries on FFA images was designed and applied on DR fundus images. These images were acquired from the diabetic patients who had referred to the Nikookari hospital and were diagnosed for diabetic retinopathy during one year. Our strategy was screening the whole image with a fixed window size, which is small enough to enclose areas with identified topographic characteristics. To discard false nominees, we also performed a thresholding operation on the screen and marked images. To validate its performance we applied our detection algorithm on 41 FFA diabetic retinopathy fundus images in which the CNP areas were manually delineated by three clinical experts. Results: Lesions were found as smooth regions with very high uniformity, low entropy, and small intensity variations in FFA images. The results of automated detection method were compared with manually marked CNP areas so achieved sensitivity of 81%, specificity of 78%, and accuracy of 91%.The result was present as a Receiver operating character (ROC) curve, which has an area under the curve (AUC) of 0.796 with 95% confidence intervals. Conclusion: This technique introduced a new automated detection algorithm to recognize non-perfusion lesions on FFA. This has potential to assist detecting and managing of ischemic retina and may be incorporated into automated grading diabetic retinopathy structures.

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Automated segmentation of retinal nonperfusion area in fluorescein angiography in retinal vein occlusion using convolutional neural networks

et al. 2020

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Purpose: Retinal vein occlusion (RVO) is the second most common cause of vision loss after diabetic retinopathy due to retinal vascular disease. Retinal nonperfusion (RNP), identified on fluorescein angiograms (FA) and appearing as hypofluorescence regions, is one of the most significant characteristics of RVO. Quantification of RNP is crucial for assessing the severity and progression of RVO. However, in current clinical practice, it is mostly conducted manually, which is time-consuming, subjective, and error-prone. The purpose of this study is to develop fully automated methods for segmentation of RNP using convolutional neural networks (CNNs). Methods: FA images from 161 patients were analyzed, and RNP areas were annotated by three independent physicians. The optimal method to use multi-physicians' labeled data to train the CNNs was evaluated. An adaptive histogram-based data augmentation method was utilized to boost the CNN performance. CNN methods based on context encoder module were developed for automated segmentation of RNP and compared with existing state-of-the-art methods. Results: The proposed methods achieved excellent agreements with physicians for segmentation of RNP in FA images. The CNN performance can be improved significantly by the proposed adaptive histogram-based data augmentation method. Using the averaged labels from physicians to train the CNNs achieved the best consensus with all physicians, with a mean accuracy of 0.883AE0.166 with fivefold cross-validation. Conclusions: We reported CNN methods to segment RNP in RVO in FA images. Our work can help improve clinical workflow, and can be useful for further investigating the association between RNP and retinal disease progression, as well as for evaluating the optimal treatments for the management of RVO.

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Extraction of Capillary Non-perfusion from Fundus Fluorescein Angiogram

Cited by 8 publications

References 11 publications

A comparative study on preprocessing techniques in diabetic retinopathy retinal images: Illumination correction and contrast enhancement

A comparative study on preprocessing techniques in diabetic retinopathy retinal images: Illumination correction and contrast enhancement

Detection of retinal capillary nonperfusion in fundus fluorescein angiogram of diabetic retinopathy

Automated segmentation of retinal nonperfusion area in fluorescein angiography in retinal vein occlusion using convolutional neural networks

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