Semi-Automated Computer Analysis of Retinal Vessels in Premature Infants

Almost all ocular and systemic diseases affect blood vessel attributes (tortuosity, length, width, and curvature). Quantitative measurements of these attributes could thus provide useful tool for diagnosing the severity of several diseases. However, it is still unclear how best to represent the attribute values of multiple vessels in a single image. Graphical user interface (GUI) is a promising step towards the development of a semi-automated computer assisted tool. The objective of this study is to develop a GUI for effective observation and robust retinal blood vessels analysis by ophthalmologists and to comprehend the distribution of vessels attributes. Blood vessels from 45 digital fundus images of infant retina are extracted, its centerline is delineated and tortuosity is analyzed from different putative and proposed techniques to provide reliable and comprehensive information for the retinal vasculature. K means clustering technique is used for classification analysis of different tortuosity metrics and its performance is evaluated based on sensitivity, specificity, and accuracy. The results are validated by comparing with expert ophthalmologists’ ground truths. Among the various proposed tortuosity metrics, one of our tortuosity indexes attains the highest classification accuracy of 91.42% with sensitivity and specificity of 86.36% and 97.82% respectively.

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

Future Computing and Informatics Journal ◽

10.1016/j.fcij.2017.10.001 ◽

2017 ◽

Vol 2 (2) ◽

pp. 103-109 ◽

Cited By ~ 24

Author(s):

Jyotiprava Dash ◽

Nilamani Bhoi

Keyword(s):

Blood Vessels ◽

Fundus Images ◽

Retinal Blood Vessels

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A Machine Learning based Approach for Segmenting Retinal Nerve Images using Artificial Neural Networks

Engineering, Technology & Applied Science Research ◽

10.48084/etasr.3666 ◽

2020 ◽

Vol 10 (4) ◽

pp. 5986-5991

Author(s):

A. N. Saeed

Keyword(s):

Machine Learning ◽

Neural Networks ◽

Diabetic Retinopathy ◽

Blood Vessels ◽

Back Propagation ◽

Texture Feature ◽

Texture Features ◽

Classification Performance ◽

Fundus Images ◽

Retinal Blood Vessels

Artificial Intelligence (AI) based Machine Learning (ML) is gaining more attention from researchers. In ophthalmology, ML has been applied to fundus photographs, achieving robust classification performance in the detection of diseases such as diabetic retinopathy, retinopathy of prematurity, etc. The detection and extraction of blood vessels in the retina is an essential part of various diagnosing problems associated with eyes, such as diabetic retinopathy. This paper proposes a novel machine learning approach to segment the retinal blood vessels from eye fundus images using a combination of color features, texture features, and Back Propagation Neural Networks (BPNN). The proposed method comprises of two steps, namely the color texture feature extraction and training the BPNN to get the segmented retinal nerves. Magenta color and correlation-texture features are given as input to the BPNN. The system was trained and tested in retinal fundus images taken from two distinct databases. The average sensitivity, specificity, and accuracy obtained for the segmentation of retinal blood vessels were 0.470%, 0.914%, and 0.903% respectively. Results obtained reveal that the proposed methodology is excellent in automated segmentation retinal nerves. The proposed segmentation methodology was able to obtain comparable accuracy with other methods.

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Non-Compactness Attribute Filtering to Extract Retinal Blood Vessels in Fundus Images

International Journal of E-Health and Medical Communications ◽

10.4018/jehmc.2010070102 ◽

2010 ◽

Vol 1 (3) ◽

pp. 16-27 ◽

Cited By ~ 6

Author(s):

I. K. E. Purnama ◽

K. Y. E. Aryanto ◽

M. H. F. Wilkinson

Keyword(s):

Diabetic Retinopathy ◽

Blood Vessels ◽

Human Vision ◽

Retinal Images ◽

Retinal Vessels ◽

Fundus Images ◽

Pathological Changes ◽

Retinal Blood Vessels ◽

Average Accuracy ◽

Filtering Approach

Retinal blood vessels can give information about abnormalities or disease by examining its pathological changes. One abnormality is diabetic retinopathy, characterized by a disorder of retinal blood vessels resulting from diabetes mellitus. Currently, diabetic retinopathy is one of the major causes of human vision abnormalities and blindness. Hence, early detection can lead to proper treatment, and segmentation of the abnormality provides a map of retinal vessels that can facilitate the assessment of the characteristics of these vessels. In this paper, the authors propose a new method, consisting of a sequence of procedures, to segment blood vessels in a retinal image. In the method, attribute filtering with a so-called Max-Tree is used to represent the image based on its gray value. The filtering process is done using the branches filtering approach in which the tree branches are selected based on the non-compactness of the nodes. The selection is started from the leaves. This experiment was performed on 40 retinal images, and utilized the manual segmentation created by an observer to validate the results. The proposed method can deliver an average accuracy of 94.21%.

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SEGMENTATION OF RETINAL BLOOD VESSELS BY TOP-HAT MULTI-SCALE DETECTION FOR OPTIC DISC REMOVAL

Jurnal Teknologi ◽

10.11113/jt.v77.6226 ◽

2015 ◽

Vol 77 (6) ◽

Cited By ~ 2

Author(s):

Ain Nazari ◽

Mohd Marzuki Mustafa ◽

Mohd Asyraf Zulkifley

Keyword(s):

Blood Vessels ◽

Optic Disc ◽

System Development ◽

Computer Assisted ◽

Fundus Image ◽

Multi Scale ◽

Analysis Process ◽

Online Databases ◽

Retinal Blood Vessels ◽

Scale Detection

Nowadays, an automatic retinal vessels segmentation is important component in computer assisted system to detect numerous eye abnormalities. There are various sizes of the retinal blood vessels captured from fundus image modality, which can be detected by using multi-scale approach. However, the main limitation of the current multi-scale approaches is the inability to remove the optic disc from the detected blood vessels. In this paper, a hybrid of multi-scale detection with pre-processing approach is proposed so that clearer vessel segmentation can be obtained. The proposed method embedded with a pre-processing phase that includes four series of processes that include Top-hat transformation as the main part. This technique will reduce the influence of the structure of optic disc and enhance the contrast of the vessel from the background. Then, the result from the pre-processing phase will be fed to the multi-scale detection to perform the segmentation. The proposed method is evaluated on two publicly available online databases: HRF and DRIVE. On HRF database, the best obtained precision and specificity values are 0.9689 and 0.9989, respectively. Meanwhile, for DRIVE database, the system performs well in all performance measures: precision, specificity, accuracy and error with the best values of 0.7541, 0.9739, 0.9510 and 0.0490, respectively. In conclusion, the proposed method is able to filter the unwanted optical disc from the fundus image effectively. Thus, retinal blood vessel image can be used for further analysis process and beneficial for pre-screening system development.

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Automatic Evaluation of Plus Disease in Retinopathy of Prematurity

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.530-531.189 ◽

2014 ◽

Vol 530-531 ◽

pp. 189-196

Author(s):

Rashmi Turior ◽

Bunyarit Uyyanonvara ◽

Pornthep Chutinantvarodom

Keyword(s):

Blood Vessels ◽

Retinopathy Of Prematurity ◽

Predictive Value ◽

Automatic Evaluation ◽

Predictive Values ◽

Image Preprocessing ◽

Retinal Blood Vessels ◽

Width Measurement ◽

Sensitivity Specificity ◽

Delay In Treatment

Abnormal dilation and tortuosity of retinal blood vessels are the primary signs of plus disease in retinopathy of prematurity. Timely prognosis could help reduce the delay in treatment and the risk of retinal detachment. Our objectives is to determine whether tortuosity and dilation sufficient for plus disease could be assessed most accurately by considering only arterioles, venules, or both. Tortuosity estimation and width measurement is done using previously proposed methods. Image preprocessing is applied before the two features namely, tortuosity and width of blood vessels are estimated to supply as input parameters for classification using K-means clustering technique. The results are validated by comparing with expert ophthalmologists ground truths. Performance is evaluated based on measures as sensitivity, specificity, predictive values and accuracy. The sensitivity, specificity, positive predictive value, negative predictive value and accuracy values obtained when considering both the arteriolar tortuosity and venous dilation are 85.86%, 90.74%, 88.76%, 88.28% and 88.50% respectively.

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Hybrid UNet Architecture based on Residual Learning of Fundus Images for Retinal Vessel Segmentation

Journal of Physics Conference Series ◽

10.1088/1742-6596/2070/1/012104 ◽

2021 ◽

Vol 2070 (1) ◽

pp. 012104

Author(s):

Sushma Nagdeote ◽

Sapna Prabhu

Keyword(s):

Blood Vessels ◽

Retinal Vessel ◽

Vessel Segmentation ◽

Intensity Difference ◽

Fundus Images ◽

The Past ◽

Retinal Blood Vessels ◽

Retinal Fundus Image ◽

Retinal Vessel Segmentation ◽

Retinal Fundus

Abstract This paper deals with the new segmentation techniques for retinal blood vessels on fundus images. This technique aims at extracting thin vessels to reduce the intensity difference between thick and thin vessels. This paper proposes the modified UNet model by incorporating ResNet blocks into it which includes structured prediction. In this work we generate the visualization of blood vessels from retinal fundus image for two loss functions namely cross entropy loss and Dice loss where the network classifies several pixels simultaneously. The results shows higher accuracy by considering a much more expressive UNet algorithm and outperforms the past algorithms for Retinal Vessel Segmentation. The benefits of this approach will be demonstrated empirically.

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