scholarly journals Extraction of Blood Vessels in Fundus Images of Retina through Hybrid Segmentation Approach

Mathematics ◽  
2019 ◽  
Vol 7 (2) ◽  
pp. 169 ◽  
Author(s):  
Ramakrishnan Sundaram ◽  
Ravichandran KS ◽  
Premaladha Jayaraman ◽  
Venkatraman B
Keyword(s):  
Blood Vessels ◽  
Ground Truth ◽  
Segmentation Algorithm ◽  
Morphological Operations ◽  
Fundus Images ◽  
Fundus Image ◽  
Segmentation Approach ◽  
Vessel Enhancement ◽  
Morphological Operator ◽  
Hybrid Segmentation

A hybrid segmentation algorithm is proposed is this paper to extract the blood vesselsfrom the fundus image of retina. Fundus camera captures the posterior surface of the eye and thecaptured images are used to diagnose diseases, like Diabetic Retinopathy, Retinoblastoma, Retinalhaemorrhage, etc. Segmentation or extraction of blood vessels is highly required, since the analysisof vessels is crucial for diagnosis, treatment planning, and execution of clinical outcomes in the fieldof ophthalmology. It is derived from the literature review that no unique segmentation algorithm issuitable for images of different eye-related diseases and the degradation of the vessels differ frompatient to patient. If the blood vessels are extracted from the fundus images, it will make thediagnosis process easier. Hence, this paper aims to frame a hybrid segmentation algorithmexclusively for the extraction of blood vessels from the fundus image. The proposed algorithm ishybridized with morphological operations, bottom hat transform, multi-scale vessel enhancement(MSVE) algorithm, and image fusion. After execution of the proposed segmentation algorithm, thearea-based morphological operator is applied to highlight the blood vessels. To validate theproposed algorithm, the results are compared with the ground truth of the High-Resolution Fundus(HRF) images dataset. Upon comparison, it is inferred that the proposed algorithm segments theblood vessels with more accuracy than the existing algorithms.

scholarly journals Severity Level Detection in Retinal Fundus Images for Diabetic Retinopathy

2020 ◽  
Vol 9 (4) ◽  
pp. 1298-1302
Keyword(s):  
Diabetic Retinopathy ◽  
Blood Vessels ◽  
Gabor Filter ◽  
Morphological Operations ◽  
Fundus Images ◽  
Fundus Image ◽  
Severity Level ◽  
Chemical Solutions ◽  
Processing Techniques ◽  
Retinal Fundus

Diabetic Retinopathy is the disease caused for diabetic people which doesn’t have symptoms in the first phase. As it progresses, it becomes symptomatic. This disease, sometimes, might lead to complete blindness. Red lesions contain microaneurysms, haemorrhages and exudates. This work focuses on detection of red lesions in fundus image. Ophthalmologists use pupil dilation of chemical solutions in order to detect the abnormality which takes time and also causes irritation to patients. Image processing techniques are used to avoid these limitations. Morphological operations are used to identify the pixels belonging to the red lesions. Gabor filter is used for separating the blood vessels. Some spots are formed near macular region because blood vessels become leaky which leads to exudates. The severity level of the disease is determined by finding the distance between lesions and macular region. The disease is considered as severe if the distance between them is closer and confirms as less if the distance is far.

scholarly journals BLOOD VESSELS SEGMENTATION METHOD FOR RETINAL FUNDUS IMAGES BASED ON ADAPTIVE PRINCIPAL CURVATURE AND IMAGE DERIVATIVE OPERATORS

2019 ◽  
Vol XLII-2/W12 ◽  
pp. 211-218 ◽  
Author(s):  
D. N. H. Thanh ◽  
D. Sergey ◽  
V. B. Surya Prasath ◽  
N. H. Hai
Keyword(s):  
Blood Vessels ◽  
Principal Curvature ◽  
Adult Population ◽  
Ground Truth ◽  
Common Disease ◽  
Fundus Images ◽  
Central Difference ◽  
Contour Matching ◽  
Retinal Fundus Images ◽  
Retinal Fundus

<p><strong>Abstract.</strong> Diabetes is a common disease in the modern life. According to WHO’s data, in 2018, there were 8.3% of adult population had diabetes. Many countries over the world have spent a lot of finance, force to treat this disease. One of the most dangerous complications that diabetes can cause is the blood vessel lesion. It can happen on organs, limbs, eyes, etc. In this paper, we propose an adaptive principal curvature and three blood vessels segmentation methods for retinal fundus images based on the adaptive principal curvature and images derivatives: the central difference, the Sobel operator and the Prewitt operator. These methods are useful to assess the lesion level of blood vessels of eyes to let doctors specify the suitable treatment regimen. It also can be extended to apply for the blood vessels segmentation of other organs, other parts of a human body. In experiments, we handle proposed methods and compare their segmentation results based on a dataset – DRIVE. Segmentation quality assessments are computed on the Sorensen-Dice similarity, the Jaccard similarity and the contour matching score with the given ground truth that were segmented manually by a human.</p>

scholarly journals SEGMENTING THE EYE FUNDUS IMAGES FOR IDENTIFICATION OF BLOOD VESSELS

2012 ◽  
Vol 17 (1) ◽  
pp. 21-30 ◽  
Author(s):  
Gediminas Balkys ◽  
Gintautas Dzemyda
Keyword(s):  
Blood Vessels ◽  
Binary Classification ◽  
Threshold Value ◽  
Retinal Images ◽  
Fundus Images ◽  
Fundus Image ◽  
Image Characteristics ◽  
Selection For ◽  
Binarization Method ◽  
Local Image

Retinal (eye fundus) images are widely used for diagnostic purposes by ophthalmologists. The normal features of eye fundus images include the optic nerve disc, fovea and blood vessels. Algorithms for identifying blood vessels in the eye fundus image generally fall into two classes: extraction of vessel information and segmentation of vessel pixels. Algorithms of the first group start on known vessel point and trace the vasculature structure in the image. Algorithms of the second group perform a binary classification (vessel or non-vessel, i.e. background) in accordance of some threshold. We focus here on the binarization [4] methods that adapt the threshold value on each pixel to the global/local image characteristics. Global binarization methods [5] try to find a single threshold value for the whole image. Local binarization methods [3] compute thresholds individually for each pixel using information from the local neighborhood of the pixel. In this paper, we modify and improve the Sauvola local binarization method [3] by extending its abilities to be applied for eye fundus pictures analysis. This method has been adopted for automatic detection of blood vessels in retinal images. We suggest automatic parameter selection for Sauvola method. Our modification allows determine/extract the blood vessels almost independently of the brightness of the picture.

A novel hybrid segmentation approach for optic papilla detection in high resolution fundus images of retina

2020 ◽  
Vol 79 (31-32) ◽  
pp. 23531-23545
Author(s):  
Ramakrishnan Sundaram ◽  
K.S Ravichandran ◽  
Premaladha Jayaraman ◽  
B Venkatraman
Keyword(s):  
High Resolution ◽  
Fundus Images ◽  
Segmentation Approach ◽  
Hybrid Segmentation

scholarly journals A robust algorithm for optic disc segmentation and fovea detection in retinal fundus images

2017 ◽  
Vol 3 (2) ◽  
pp. 533-537 ◽  
Author(s):  
Caterina Rust ◽  
Stephanie Häger ◽  
Nadine Traulsen ◽  
Jan Modersitzki
Keyword(s):  
Success Rate ◽  
Optic Disc ◽  
Ground Truth ◽  
Morphological Operations ◽  
Fundus Images ◽  
Iterative Thresholding ◽  
Fuzzy C Means Clustering ◽  
Multiple Regions ◽  
Retinal Fundus Images ◽  
Retinal Fundus

AbstractAccurate optic disc (OD) segmentation and fovea detection in retinal fundus images are crucial for diagnosis in ophthalmology. We propose a robust and broadly applicable algorithm for automated, robust, reliable and consistent fovea detection based on OD segmentation. The OD segmentation is performed with morphological operations and Fuzzy C Means Clustering combined with iterative thresholding on a foreground segmentation. The fovea detection is based on a vessel segmentation via morphological operations and uses the resulting OD segmentation to determine multiple regions of interest. The fovea is determined from the largest, vessel-free candidate region. We have tested the novel method on a total of 190 images from three publicly available databases DRIONS, Drive and HRF. Compared to results of two human experts for DRIONS database, our OD segmentation yielded a dice coefficient of 0.83. Note that missing ground truth and expert variability is an issue. The new scheme achieved an overall success rate of 99.44% for OD detection and an overall success rate of 96.25% for fovea detection, which is superior to state-of-the-art approaches.

scholarly journals Retinal Fundus Image Blood Vessels Segmentation via Object-Oriented Metadata Structures

2018 ◽  
Vol 7 (4.33) ◽  
pp. 110
Author(s):  
Ahmad Firdaus Ahmad Fadzil ◽  
Zaaba Ahmad ◽  
Noor Elaiza Abd Khalid ◽  
Shafaf Ibrahim
Keyword(s):  
Blood Vessels ◽  
Visual Information ◽  
Object Oriented ◽  
Processing Technique ◽  
Image Processing Technique ◽  
Fundus Images ◽  
Fundus Image ◽  
Retinal Fundus Image ◽  
Retinal Fundus Images ◽  
Retinal Fundus

Retinal fundus image is a crucial tool for ophthalmologists to diagnose eye-related diseases. These images provide visual information of the interior layer of the retina structures such as optic disc, optic cup, blood vessels and macula that can assist ophthalmologist in determining the health of an eye. Segmentation of blood vessels in fundus images is one of the most fundamental phase in detecting diseases such as diabetic retinopathy. However, the ambiguity of the retina structures in the retinal fundus images presents a challenge for researcher to segment the blood vessels. Extensive pre-processing and training of the images is necessary for precise segmentation, which is very intricate and laborious. This paper proposes the implementation of object-oriented-based metadata (OOM) structures of each pixel in the retinal fundus images. These structures comprise of additional metadata towards the conventional red, green, and blue data for each pixel within the images. The segmentation of the blood vessels in the retinal fundus images are performed by considering these additional metadata that enunciates the location, color spaces, and neighboring pixels of each individual pixel. From the results, it is shown that accurate segmentation of retinal fundus blood vessels can be achieved by purely employing straightforward thresholding method via the OOM structures without extensive pre-processing image processing technique or data training.      

scholarly journals Optic Cup Segmentation using U-Net Architecture on Retinal Fundus Image

2020 ◽  
Vol 4 (02) ◽  
pp. 105-109
Author(s):  
Pulung Hendro Prastyo ◽  
Amin Siddiq Sumi ◽  
Annis Nuraini
Keyword(s):  
Ground Truth ◽  
Batch Size ◽  
Fundus Images ◽  
Fundus Image ◽  
Dice Coefficient ◽  
Input Size ◽  
Retinal Fundus Image ◽  
Optic Cup ◽  
Retinal Fundus Images ◽  
Retinal Fundus

Retinal fundus images are used by ophthalmologists to diagnose eye disease, such as glaucoma disease. The diagnosis of glaucoma is done by measuring changes in the cup-to-disc ratio. Segmenting the optic cup helps petrify ophthalmologists calculate the CDR of the retinal fundus image. This study proposed a deep learning approach using U-Net architecture to carry out segmentation task. This proposed method was evaluated on 650 color retinal fundus image. Then, U-Net was configured using 160 epochs, image input size = 128x128, Batch size = 32, optimizer = Adam, and loss function = Binary Cross Entropy. We employed the Dice Coefficient as the evaluator. Besides, the segmentation results were compared to the ground truth images. According to the experimental results, the performance of optic cup segmentation achieved 98.42% for the Dice coefficient and loss of 1,58%. These results implied that our proposed method succeeded in segmenting the optic cup on color retinal fundus images.

Two-Tier Grading System for NPDR Severities of Diabetic Retinopathy in Retinal Fundus Images

2020 ◽  
Vol 14 ◽  
Author(s):  
Charu Bhardwaj ◽  
Shruti Jain ◽  
Meenakshi Sood
Keyword(s):  
Diabetic Retinopathy ◽  
Statistical Analysis ◽  
Early Stage ◽  
Image Feature ◽  
Grading System ◽  
Fundus Images ◽  
Fundus Image ◽  
Retinal Fundus Images ◽  
Retinal Fundus ◽  
Timely Treatment

: Diabetic Retinopathy is the leading cause of vision impairment and its early stage diagnosis relies on regular monitoring and timely treatment for anomalies exhibiting subtle distinction among different severity grades. The existing Diabetic Retinopathy (DR) detection approaches are subjective, laborious and time consuming which can only be carried out by skilled professionals. All the patents related to DR detection and diagnoses applicable for our research problem were revised by the authors. The major limitation in classification of severities lies in poor discrimination between actual lesions, background noise and other anatomical structures. A robust and computationally efficient Two-Tier DR (2TDR) grading system is proposed in this paper to categorize various DR severities (mild, moderate and severe) present in retinal fundus images. In the proposed 2TDR grading system, input fundus image is subjected to background segmentation and the foreground fundus image is used for anomaly identification followed by GLCM feature extraction forming an image feature set. The novelty of our model lies in the exhaustive statistical analysis of extracted feature set to obtain optimal reduced image feature set employed further for classification. Classification outcomes are obtained for both extracted as well as reduced feature set to validate the significance of statistical analysis in severity classification and grading. For single tier classification stage, the proposed system achieves an overall accuracy of 100% by k- Nearest Neighbour (kNN) and Artificial Neural Network (ANN) classifier. In second tier classification stage an overall accuracy of 95.3% with kNN and 98.0% with ANN is achieved for all stages utilizing optimal reduced feature set. 2TDR system demonstrates overall improvement in classification performance by 2% and 6% for kNN and ANN respectively after feature set reduction, and also outperforms the accuracy obtained by other state of the art methods when applied to the MESSIDOR dataset. This application oriented work aids in accurate DR classification for effective diagnosis and timely treatment of severe retinal ailment.

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