Deep Classification and Segmentation Model for Vessel Extraction in Retinal Images

2018 ◽  
pp. 250-258
Author(s):  
Yicheng Wu ◽  
Yong Xia ◽  
Yanning Zhang
Keyword(s):  
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.

Contourlet transform-based sharpening enhancement of retinal images and vessel extraction application

2013 ◽  
Vol 58 (1) ◽  
Author(s):  
Anand Swaminathan ◽  
Shantha Selva Kumari Ramapackiam ◽  
Thivya Thiraviam ◽  
Jeeva Selvaraj
Keyword(s):  
Contourlet Transform ◽  
Retinal Images ◽  
Vessel Extraction

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.

Vessel extraction in retinal images using multilevel line detection

2012 ◽  
Author(s):  
S. Rattathanapad ◽  
P. Mittrapiyanuruk ◽  
P. Kaewtrakulpong ◽  
B. Uyyanonvara ◽  
C. Sinthanayothin
Keyword(s):  
Line Detection ◽  
Retinal Images ◽  
Vessel Extraction

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
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