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

2015 ◽  
Vol 77 (6) ◽  
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.  

A Study on the Examination of RGB Scale Retinal Pictures Using Recent Methodologies

2020 ◽  
pp. 198-220
Author(s):  
A. Swarnalatha ◽  
K. Palani Thanaraj ◽  
A. Sheryl Oliver ◽  
M. Esther Hannah
Keyword(s):  
Optic Disc ◽  
Matched Filter ◽  
Retinal Disease ◽  
Fundus Image ◽  
Two Phase ◽  
Multi Scale ◽  
Level Set Segmentation ◽  
Kapur’S Entropy ◽  
Disease Condition ◽  
Benchmark Datasets

Retinal disease/condition examination is one of the significant areas of the medical field. A variety of retinal abnormality assessments based on fundus image-assisted trials are widely proposed by the researchers to examine the parts of the retina. Recently, traditional and soft computing-based approaches are executed to inspect the optic disc and the blood vessels of the retina to discover disease/damages. This work implements (i) A two-phase methodology based on Jaya Algorithm (JA) and Kapur's Entropy (KE) thresholding and level-set segmentation for the optic disc evaluation and (ii) JA-based Multi-scale Matched Filter (MMF) for the blood vessel assessment. During this analysis, various benchmark datasets such as RIM-ONE, DRIVE, and STARE are considered. The experimental study substantiates that JA-assisted retinal picture examination offers better results than other related existing methodologies.

scholarly journals Segmenting Retinal Blood Vessels with Gabor Filter and Automatic Binarization

2018 ◽  
Vol 7 (4.11) ◽  
pp. 163 ◽  
Author(s):  
Aziah Ali ◽  
Aini Hussain ◽  
Wan Mimi Diyana Wan Zaki
Keyword(s):  
Blood Vessels ◽  
Gabor Filter ◽  
Retinal Disease ◽  
Gabor Wavelet ◽  
Fundus Image ◽  
Small Blood Vessel ◽  
Binary Images ◽  
Filter Output ◽  
Retinal Blood Vessels ◽  
Otsu’S Method

For timely diagnosis of retinal disease, routine retinal monitoring of people with high risk should be put in place. To assist the ophthalmologists in performing retinal analysis efficiently and accurately, numerous studies have been conducted to propose an automated retinal diagnosis system. One of the crucial steps for such a system is accurate detection of retinal blood vessels from retinal image. In this paper, we investigated the use of automatic binarization methods on pre-processed fundus image to detect retinal blood vessels. Three methods for binarization were investigated in this study, namely Otsu’s method, ISODATA and K-means clustering method. The resulting binarized output indicated good detection of large vessels but most of the smaller vessels were left undetected. To address this issue, Gabor wavelet filter was used to enhance the small blood vessel structures before binarization of the filter output. Combining the binary images from both binarization with and without Gabor filter resulted in significant improvement of the overall detection rate of the retinal blood vessels. The proposed method proved to be comparable to other unsupervised techniques in the literature when validated using the publicly available fundus image database, DRIVE.  

scholarly journals CMM-Net: Contextual Multi-Scale Multi-Level Network for Efficient Biomedical Image Segmentation

2021 ◽  
Author(s):  
Mohammed Al-masni ◽  
Dong-Hyun Kim
Keyword(s):  
Image Segmentation ◽  
Blood Vessels ◽  
Medical Image ◽  
Spatial Scales ◽  
Medical Image Analysis ◽  
Skin Lesions ◽  
Medical Image Segmentation ◽  
Multi Scale ◽  
Retinal Blood Vessels ◽  
Multi Level

Abstract Medical image segmentation of tissue abnormalities, key organs, or blood vascular system is of great significance for any computerized diagnostic system. However, automatic segmentation in medical image analysis is a challenging task since it requires sophisticated knowledge of the target organ anatomy. This paper develops an end-to-end deep learning segmentation method called Contextual Multi-Scale Multi-Level Network (CMM-Net). The main idea is to fuse the global contextual features of multiple spatial scales at every contracting convolutional network level in the U-Net. Also, we re-exploit the dilated convolution module that enables an expansion of the receptive field with different rates depending on the size of feature maps throughout the networks. In addition, an augmented testing scheme referred to as Inversion Recovery (IR) which uses logical “OR” and “AND” operators is developed. The proposed segmentation network is evaluated on three medical imaging datasets, namely ISIC 2017 for skin lesions segmentation from dermoscopy images, DRIVE for retinal blood vessels segmentation from fundus images, and BraTS 2018 for brain gliomas segmentation from MR scans. The experimental results showed superior state-of-the-art performance with overall dice similarity coefficients of 85.78%, 80.27%, and 88.96% on the segmentation of skin lesions, retinal blood vessels, and brain tumors, respectively. The proposed CMM-Net is inherently general and could be efficiently applied as a robust tool for various medical image segmentations.

Semi-Automated Computer Analysis of Retinal Vessels in Premature Infants

2012 ◽  
Vol 241-244 ◽  
pp. 2962-2968
Author(s):  
Rashmi Turior ◽  
Pornthep Chutinantvaron ◽  
Bunyarit Uyyanonvara
Keyword(s):  
Blood Vessels ◽  
Computer Assisted ◽  
Fundus Images ◽  
Single Image ◽  
Quantitative Measurements ◽  
Comprehensive Information ◽  
Retinal Blood Vessels ◽  
Length Width ◽  
Almost All ◽  
Sensitivity Specificity

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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