An unsupervised coarse-to-fine algorithm for blood vessel segmentation in fundus images

Neto, Luiz Cmara; Ramalho, Geraldo Luis Bezerra; Neto, J. F. S. Rocha; Veras, Rodrigo; Medeiros, Fátima N. S. de

doi:10.1016/j.eswa.2017.02.015

Cited by 98 publications

(26 citation statements)

References 26 publications

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“…Among other methods, our method achieved the best performance in terms of Sp (0.9770), Acc (0.9628) and AUC (0.9801) . Comparable result was obtained with regard to Se between our method and [6]. The binary vessel map results are in Figures 7 and 8, which show our method works well on both healthy and pathological cases.…”

Section: The Improvement Of the Data Qualitymentioning

confidence: 61%

“…Simultaneously, they applied non-orthogonal wavelet transformation based on Log-Gabor to further eliminate the noise. Neto et al [6] first enhanced the image contrast via Gaussian smoothing and morphological top-hat transformation. Then, they set a local threshold according to the image's gray intensity and spatial dependencies between pixels to extract vessel preliminarily.…”

Section: Related Workmentioning

confidence: 99%

“…The presence of the lesions and exudates in pathologic images undoubtedly further complicate the segmentation task. The computer-assisted method is a effective way to address the task of fundus vessel segmentation with satisfactory performance [6][7][8].…”

Section: Introductionmentioning

confidence: 99%

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A Coarse-to-Fine Fully Convolutional Neural Network for Fundus Vessel Segmentation

Chen

et al. 2018

Symmetry

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Fundus vessel analysis is a significant tool for evaluating the development of retinal diseases such as diabetic retinopathy and hypertension in clinical practice. Hence, automatic fundus vessel segmentation is essential and valuable for medical diagnosis in ophthalmopathy and will allow identification and extraction of relevant symmetric and asymmetric patterns. Further, due to the uniqueness of fundus vessel, it can be applied in the field of biometric identification. In this paper, we remold fundus vessel segmentation as a task of pixel-wise classification task, and propose a novel coarse-to-fine fully convolutional neural network (CF-FCN) to extract vessels from fundus images. Our CF-FCN is aimed at making full use of the original data information and making up for the coarse output of the neural network by harnessing the space relationship between pixels in fundus images. Accompanying with necessary pre-processing and post-processing operations, the efficacy and efficiency of our CF-FCN is corroborated through our experiments on DRIVE, STARE, HRF and CHASE DB1 datasets. It achieves sensitivity of 0.7941, specificity of 0.9870, accuracy of 0.9634 and Area Under Receiver Operating Characteristic Curve (AUC) of 0.9787 on DRIVE datasets, which surpasses the state-of-the-art approaches.

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Section: The Improvement Of the Data Qualitymentioning

confidence: 61%

Section: Related Workmentioning

confidence: 99%

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A Coarse-to-Fine Fully Convolutional Neural Network for Fundus Vessel Segmentation

Chen

et al. 2018

Symmetry

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“…However, the approach had less significance in the detection of lesions that contribute to an incorrect vascular structure. Neto et al in [43] presented an unsupervised coarse segmentation approach for vessel detection with an average accuracy of 87%. They incorporated multiple concepts, i.e., mathematical morphology, curvature, and spatial dependency, with the coarse-to-fine method to accurately define thin and elongated vessels from vessel pixels.…”

Section: Blood Vessel Extractionmentioning

confidence: 99%

Recent Development on Detection Methods for the Diagnosis of Diabetic Retinopathy

Qureshi

Abbas

2019

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Diabetic retinopathy (DR) is a complication of diabetes that exists throughout the world. DR occurs due to a high ratio of glucose in the blood, which causes alterations in the retinal microvasculature. Without preemptive symptoms of DR, it leads to complete vision loss. However, early screening through computer-assisted diagnosis (CAD) tools and proper treatment have the ability to control the prevalence of DR. Manual inspection of morphological changes in retinal anatomic parts are tedious and challenging tasks. Therefore, many CAD systems were developed in the past to assist ophthalmologists for observing inter- and intra-variations. In this paper, a recent review of state-of-the-art CAD systems for diagnosis of DR is presented. We describe all those CAD systems that have been developed by various computational intelligence and image processing techniques. The limitations and future trends of current CAD systems are also described in detail to help researchers. Moreover, potential CAD systems are also compared in terms of statistical parameters to quantitatively evaluate them. The comparison results indicate that there is still a need for accurate development of CAD systems to assist in the clinical diagnosis of diabetic retinopathy.

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“…The main contribution of Ref. 31 is an unsupervised method to detect blood vessels in fundus images using a coarse-to-fine approach. This strategy combines Gaussian smoothing, a morphological top-hat operator, and vessel contrast enhancement for background homogenization and noise reduction.…”

Section: Prior Workmentioning

confidence: 99%

Blood vessel segmentation in modern wide-field retinal images in the presence of additive Gaussian noise

2018

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Retinal blood vessels indicate some serious health ramifications, such as cardiovascular disease and stroke. Thanks to modern imaging technology, high-resolution images provide detailed information to help analyze retinal vascular features before symptoms associated with such conditions fully develop. Additionally, these retinal images can be used by ophthalmologists to facilitate diagnosis and the procedures of eye surgery. A fuzzy noise reduction algorithm was employed to enhance color images corrupted by Gaussian noise. The present paper proposes employing a contrast limited adaptive histogram equalization to enhance illumination and increase the contrast of retinal images captured from state-of-the-art cameras. Possessing directional properties, the multistructure elements method can lead to high-performance edge detection. Therefore, multistructure elements-based morphology operators are used to detect high-quality image ridges. Following this detection, the irrelevant ridges, which are not part of the vessel tree, were removed by morphological operators by reconstruction, attempting also to keep the thin vessels preserved. A combined method of connected components analysis (CCA) in conjunction with a thresholding approach was further used to identify the ridges that correspond to vessels. The application of CCA can yield higher efficiency when it is locally applied rather than applied on the whole image. The significance of our work lies in the way in which several methods are effectively combined and the originality of the database employed, making this work unique in the literature. Computer simulation results in wide-field retinal images with up to a 200-deg field of view are a testimony of the efficacy of the proposed approach, with an accuracy of 0.9524.

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An unsupervised coarse-to-fine algorithm for blood vessel segmentation in fundus images

Cited by 98 publications

References 26 publications

A Coarse-to-Fine Fully Convolutional Neural Network for Fundus Vessel Segmentation

A Coarse-to-Fine Fully Convolutional Neural Network for Fundus Vessel Segmentation

Recent Development on Detection Methods for the Diagnosis of Diabetic Retinopathy

Blood vessel segmentation in modern wide-field retinal images in the presence of additive Gaussian noise

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