Blood vessel extraction and optic disc removal using curvelet transform and kernel fuzzy c-means

Kar, Sudeshna Sil; Maity, Santi P.

doi:10.1016/j.compbiomed.2015.12.018

Cited by 42 publications

(27 citation statements)

References 30 publications

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“…where L λ denotes the estimated curvelet coefficient after denoising, L λ is the original curvelet coefficient, σ n ab is the noise variance of the coefficients at scale a and angle b which is estimated by any standard noise variance estimation method [21]. However, L λ = L λ for an image with σ n = 0.…”

Section: Proposed Methodsmentioning

confidence: 99%

Gradation of diabetic retinopathy on reconstructed image using compressed sensing

Kar

Maity

2018

IET Image Processing

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Section: Proposed Methodsmentioning

confidence: 99%

Gradation of diabetic retinopathy on reconstructed image using compressed sensing

Kar

Maity

2018

IET Image Processing

Self Cite

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“…In their algorithm, the authors apply two-dimensional matched filters with LoG kernel functions. Kar and Maity (2016) developed an approach combined with curvelet transformation, matched filtering, and Laplacian Gaussian filter. Their experimental results reveal that the method performed well on both pathological and noisy retinal images.…”

Section: Retinal Image Segmentationmentioning

confidence: 99%

Analysis of Video Retinal Angiography With Deep Learning and Eulerian Magnification

Laha

LaLonde

Carmack

et al. 2020

Front. Comput. Sci.

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The aim of this research is to present a novel computer-aided decision support tool in analyzing, quantifying, and evaluating the retinal blood vessel structure from fluorescein angiogram (FA) videos. Methods: The proposed method consists of three phases: (i) image registration for large motion removal from fluorescein angiogram videos, followed by (ii) retinal vessel segmentation, and lastly, (iii) segmentation-guided video magnification. In the image registration phase, individual frames of the video are spatiotemporally aligned using a novel wavelet-based registration approach to compensate for the global camera and patient motion. In the second phase, a capsule-based neural network architecture is employed to perform the segmentation of retinal vessels for the first time in the literature. In the final phase, a segmentation-guided Eulerian video magnification is proposed for magnifying subtle changes in the retinal video produced by blood flow through the retinal vessels. The magnification is applied only to the segmented vessels, as determined by the capsule network. This minimizes the high levels of noise present in these videos and maximizes useful information, enabling ophthalmologists to more easily identify potential regions of pathology. Results: The collected fluorescein angiogram video dataset consists of 1, 402 frames from 10 normal subjects (prospective study). Experimental results for retinal vessel segmentation show that the capsule-based algorithm outperforms a state-of-the-art convolutional neural networks (U-Net), obtaining a higher dice coefficient (85.94%) and sensitivity (92.36%) while using just 5% of the network parameters. Qualitative analysis of these videos was performed after the final phase by expert ophthalmologists, supporting the claim that artificial intelligence assisted decision support tool can be helpful for providing a better analysis of blood flow dynamics. Conclusions: The authors introduce a novel computational tool, combining a wavelet-based video registration method with a deep learning capsule-based retinal vessel segmentation algorithm and a Eulerian video magnification technique to quantitatively and qualitatively analyze FA videos. To authors' best knowledge, this is the first-ever development of such a computational tool to assist ophthalmologists with analyzing blood flow in FA videos.

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“…Fraz et al used quantitative analysis of retinal vessel topology and size (QUARTZ), where vessel segmentation is carried out using a line detection scheme in combination with hysteresis morphological reconstruction based on a bi-threshold procedure [24]. Kar et al used automatic blood vessel extraction using a matched filtering-based integrated system, which uses a curvelet transform and fuzzy c-means algorithm to separate vessels from the background [25]. Another recent example of an unsupervised approach was illustrated by Zhao et al, who used a framework with three steps.…”

Section: Related Workmentioning

confidence: 99%

Aiding the Diagnosis of Diabetic and Hypertensive Retinopathy Using Artificial Intelligence-Based Semantic Segmentation

Arsalan

Owais

Mahmood

et al. 2019

JCM

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Automatic segmentation of retinal images is an important task in computer-assisted medical image analysis for the diagnosis of diseases such as hypertension, diabetic and hypertensive retinopathy, and arteriosclerosis. Among the diseases, diabetic retinopathy, which is the leading cause of vision detachment, can be diagnosed early through the detection of retinal vessels. The manual detection of these retinal vessels is a time-consuming process that can be automated with the help of artificial intelligence with deep learning. The detection of vessels is difficult due to intensity variation and noise from non-ideal imaging. Although there are deep learning approaches for vessel segmentation, these methods require many trainable parameters, which increase the network complexity. To address these issues, this paper presents a dual-residual-stream-based vessel segmentation network (Vess-Net), which is not as deep as conventional semantic segmentation networks, but provides good segmentation with few trainable parameters and layers. The method takes advantage of artificial intelligence for semantic segmentation to aid the diagnosis of retinopathy. To evaluate the proposed Vess-Net method, experiments were conducted with three publicly available datasets for vessel segmentation: digital retinal images for vessel extraction (DRIVE), the Child Heart Health Study in England (CHASE-DB1), and structured analysis of retina (STARE). Experimental results show that Vess-Net achieved superior performance for all datasets with sensitivity (Se), specificity (Sp), area under the curve (AUC), and accuracy (Acc) of 80.22%, 98.1%, 98.2%, and 96.55% for DRVIE; 82.06%, 98.41%, 98.0%, and 97.26% for CHASE-DB1; and 85.26%, 97.91%, 98.83%, and 96.97% for STARE dataset.

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Blood vessel extraction and optic disc removal using curvelet transform and kernel fuzzy c-means

Cited by 42 publications

References 30 publications

Gradation of diabetic retinopathy on reconstructed image using compressed sensing

Gradation of diabetic retinopathy on reconstructed image using compressed sensing

Analysis of Video Retinal Angiography With Deep Learning and Eulerian Magnification

Aiding the Diagnosis of Diabetic and Hypertensive Retinopathy Using Artificial Intelligence-Based Semantic Segmentation

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