A thresholding based technique to extract retinal blood vessels from fundus images

Dash, Jyotiprava; Bhoi, Nilamani

doi:10.1016/j.fcij.2017.10.001

Cited by 85 publications

(34 citation statements)

References 17 publications

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“…Silva et al [115] evaluated 126 images from 69 patients for the early screening of DR. Their application detected MA and HM features with a kappa value of a 0.10 ratio. Dash et al [116] developed a DR screening tool based on the segmentation of vascular structures. Their software used 58 images obtained from the DRIVE and CHASE_DB1 datasets and achieved a 0.955 and 0.954 accuracy, respectively.…”

Section: Diabetic Retinopathy Detection Systemsmentioning

confidence: 99%

Recent Development on Detection Methods for the Diagnosis of Diabetic Retinopathy

Qureshi

Abbas

2019

Symmetry

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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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Section: Diabetic Retinopathy Detection Systemsmentioning

confidence: 99%

Recent Development on Detection Methods for the Diagnosis of Diabetic Retinopathy

Qureshi

Abbas

2019

Symmetry

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“…To avoid the effect of the false positive regions in the preliminary extraction result, they further applied morphology to rebuild a gray-scale image curvature chart for optimizing the vessel segmentation performance. Dash et al [14] adopted contrast limited adaptive histogram equalization and median filter to enhance the image, and used mean-C thresholding to obtain the final result. Odstrcilik et al [15] used an improved matched filtering for vessel segmentation.…”

Section: Related Workmentioning

confidence: 99%

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

Chen

et al. 2018

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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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“…Research on retinal image segmentation has been done by [22], [23] aiming to simplify or change image representation into something that is easier to analyze. [24] who have conducted a retinal image study by proposing a computerized technique for extracting retinal vessels. In addition, [25] and [26] conducted a retinal image study for classification segmentation of retinal disease types using different methods.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Multilevel Thresholding and Improved Harmony Search Algorithm for Segmentation

Erwin

Saparudin

Saputri

2018

IJECE

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This paper proposes a new method for image segmentation is hybrid multilevel thresholding and improved harmony search algorithm. Improved harmony search algorithm which is a method for finding vector solutions by increasing its accuracy. The proposed method looks for a random candidate solution, then its quality is evaluated through the Otsu objective function. Furthermore, the operator continues to evolve the solution candidate circuit until the optimal solution is found. The dataset used in this study is the retina dataset, tongue, lenna, baboon, and cameraman. The experimental results show that this method produces the high performance as seen from peak signal-to-noise ratio analysis (PNSR). The PNSR result for retinal image averaged 40.342 dB while for the average tongue image 35.340 dB. For lenna, baboon and cameramen produce an average of 33.781 dB, 33.499 dB, and 34.869 dB. Furthermore, the process of object recognition and identification is expected to use this method to produce a high degree of accuracy.

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A thresholding based technique to extract retinal blood vessels from fundus images

Cited by 85 publications

References 17 publications

Recent Development on Detection Methods for the Diagnosis of Diabetic Retinopathy

Recent Development on Detection Methods for the Diagnosis of Diabetic Retinopathy

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

Hybrid Multilevel Thresholding and Improved Harmony Search Algorithm for Segmentation

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