Segmentation of Retinal Blood Vessel Using Gabor Filter and Extreme Learning Machines

被引：9

作者：

Aslan, Muhammet Fatih ^{[1
]}

Ceylan, Murat ^{[2
]}

Durdu, Akif ^{[2
]}

机构：

[1] Karamanoglu Mehmetbey Univ, Fac Engn, Dept Elect Elect Engn, Karaman, Turkey

[2] Konya Tech Univ, Fac Engn & Nat Sci, Dept Elect Elect Engn, Konya, Turkey

来源：

2018 INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND DATA PROCESSING (IDAP) | 2018年

关键词：

Feature extraction; vessel segmentation; Extreme learning machine; Gabor filter; Top-Hat transform; IMAGES;

D O I：

10.1109/IDAP.2018.8620890

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The process of obtaining blood vessels from the retinal fundus images plays an important role in the detection of disease in the eye. Analysis of blood vessels provides preliminary information on the presence and treatment of glaucoma, retinopathy, etc. This is why such practices are important. In this study, firstly, features were extracted from color retinal images. Adaptive threshold, Gabor filter and Top-Hat transform were used to make the blood vessel more visible during the feature extraction phase. Subsequently, the acquired features were given as input to the extreme learning machine, and as a result, retinal blood vessel was obtained. At this stage, DRIVE database was used. Twenty colored retinal fundus images were used in the train phase. Thanks to the extreme learning machine, the training process has been carried out quickly (0.42 sec). A high accuracy rate is obtained as %94.59.

引用

页数：5

共 19 条

[1] A novel approach for automatic blood vessel extraction in retinal images: complex ripplet-I transform and complex valued artificial neural network [J].