Automated blood vessel extraction using local features on retinal images

2016 ◽  
Author(s):  
Yuji Hatanaka ◽  
Kazuki Samo ◽  
Mikiya Tajima ◽  
Kazunori Ogohara ◽  
Chisako Muramatsu ◽  
...  
Keyword(s):  
Blood Vessel ◽  
Local Features ◽  
Retinal Images ◽  
Vessel Extraction

scholarly journals Extraction of Blood Vessels in Retinal Images Using Four Different Techniques

2013 ◽  
Vol 2013 ◽  
pp. 1-21 ◽  
Author(s):  
Asloob Ahmad Mudassar ◽  
Saira Butt
Keyword(s):  
Blood Vessel ◽  
Cross Sections ◽  
Retinal Images ◽  
Matched Filtering ◽  
Edge Enhancement ◽  
Promising Technique ◽  
Filtering Technique ◽  
Continuation Algorithm ◽  
Line Cross ◽  
Vessel Extraction

A variety of blood vessel extraction (BVE) techniques exist in the literature, but they do not always lead to acceptable solutions especially in the presence of anomalies where the reported work is limited. Four techniques are presented for BVE: (1) BVE using Image Line Cross-Sections (ILCS), (2) BVE using Edge Enhancement and Edge Detection (EEED), (3) BVE using Modified Matched Filtering (MMF), and (4) BVE using Continuation Algorithm (CA). These four techniques have been designed especially for abnormal retinal images containing low vessel contrasts, drusen, exudates, and other artifacts. The four techniques were applied to 30 abnormal retinal images, and the success rate was found to be (95 to 99%) for CA, (88–91%) for EEED, (80–85%) for MMF, and (74–78%) for ILCS. Application of these four techniques to 105 normal retinal images gave improved results: (99-100%) for CA, (96–98%) for EEED, (94-95%) for MMF, and (88–93%) for ILCS. Investigations revealed that the four techniques in the order of increasing performance could be arranged as ILCS, MMF, EEED, and CA. Here we demonstrate these four techniques for abnormal retinal images only. ILCS, EEED, and CA are novel additions whereas MMF is an improved and modified version of an existing matched filtering technique. CA is a promising technique.

Automated Blood Vessel Extraction Based on High-Order Local Autocorrelation Features on Retinal Images

2017 ◽  
pp. 803-810 ◽  
Author(s):  
Yuji Hatanaka ◽  
Kazuki Samo ◽  
Kazunori Ogohara ◽  
Wataru Sunayama ◽  
Chisako Muramatsu ◽  
...  
Keyword(s):  
Blood Vessel ◽  
High Order ◽  
Retinal Images ◽  
Vessel Extraction

Survey on Ocular Blood Vessel Segmentation

2017 ◽  
Vol 7 (7) ◽  
pp. 318
Author(s):  
R. Hannah Roseline ◽  
R. Jemina Priyadarsini
Keyword(s):  
Blood Vessel ◽  
Retinal Image ◽  
Vascular Diseases ◽  
Diagnostic Procedures ◽  
The Body ◽  
Retinal Images ◽  
Retinal Blood Vessels ◽  
The Subject ◽  
Cardio Vascular ◽  
Vessel Extraction

The eye is sometimes said to provide a window into the health of a person for it is only with the eye that one can actually see the exposed flesh of the subject without using invasive procedures. There are a number of diseases, particularly vascular disease that leave telltale markers in the retina. The retina can be photographed relatively straightforwardly with a funds camera and now with retinal image processing there is much interest in computer analysis of retinal images for identifying and quantifying the effects of diseases such as cardio vascular diseases. A retinal image provides a snapshot of what is happening inside the human body. In particular, the ceremonial of the retinal blood vessels has been shown to imitate the cardiovascular condition of the body. Retinal images provide considerable information on pathological changes caused by local ocular disease which reveals diabetes, hypertension, arteriosclerosis, cardiovascular disease and stroke. Computer-aided study of retinal image plays a central role in diagnostic procedures. However, automatic retinal segmentation is complicated by the fact that retinal images are often noisy, poorly contrasted, and the vessel widths can vary from very small to very large. So in this survey we can review various segmentation techniques to improve the accuracy in blood vessel extraction.

scholarly journals A Study of Retinal Blood Vessel Extraction from the Retinal Images

IJARCCE ◽  
2015 ◽  
Vol 4 (4) ◽  
pp. 509-511 ◽  
Author(s):  
Sandeep Singh ◽  
Dr. Dinesh Kumar
Keyword(s):  
Blood Vessel ◽  
Retinal Images ◽  
Retinal Blood Vessel ◽  
Vessel Extraction

scholarly journals Enhanced Detection of Diabetic Retinopathy using Advanced Filters

2019 ◽  
Vol 9 (2) ◽  
pp. 4470-4474
Keyword(s):  
Diabetic Retinopathy ◽  
Blood Vessel ◽  
Patient Information ◽  
Image Features ◽  
Retinal Images ◽  
Diabetic Patients ◽  
Local Entropy ◽  
Vessel Extraction ◽  
The Many ◽  
Short Time

Nowadays in India, diabetic patients are more increasing. The major issue with diabetic patients is Diabetic retinopathy which causes the loss of vision. For the ophthalmologist, it is very difficult to identify the diabetic retinopathy because of the low resolution of the eyes. For the specialists, it is easy to find the blood vessels in the retina to diagnose the many populations in a very short time. Various existing methods are used to find the abnormal retinal images of diabetic patients based on their image features. But the results are not that much accurate. In this paper, an enhanced image filter with local entropy thresholding for blood vessel extraction under different normal or abnormal conditions is proposed to improve the performance of the patient information.

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