NDLI: Retinal vessel segmentation using neural network

Content Provider	IET Digital Library
Author	Thangaraj, Sumathi Periyasamy, Vivekanandan Balaji, Ravikanth
Abstract	Automatic extraction of retinal blood vessels plays an important role in the diagnosis of many retinal diseases and also for diagnosing several complicated diseases such as stroke, hypertension and cardiovascular diseases. Due to the complex nature of retinal vessel network, the manual segmentation of vessels is a tedious task which also requires high training and skills. This study presents a new method for blood vessel segmentation in colour retinal images using supervised approach. Initially, a set of core features including Gabor filter responses, Frangi's vesselness measure (1D), local binary pattern feature (1D), Hu moment invariants (7D) and grey-level co-occurrence matrix features (3D) are considered. The neural network is trained with the different subsets of core features and it is found that the model with 13D features excluding the Hu moment invariants results in better performance. This model is used for evaluation. The proposed supervised segmentation approach is tested on publicly available structured analysis of the retina, digital retinal images for vessel extraction and CHASE_DB1 databases which contain manually labelled images. The performance of the proposed algorithm is evaluated on the basis of accuracy, sensitivity, specificity and area under the curve. The proposed technique achieves high mean accuracy and sensitivity while it is compared with the several previously proposed algorithms.
Starting Page	669
Ending Page	678
Page Count	10
ISSN	17519659
Volume Number	12
e-ISSN	17519667
Issue Number	Issue 5, May (2018)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/12/5
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2017.0284
Journal	IET Image Processing
Publisher Date	2017-12-12
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Algebra Automatic Extraction Biology And Medical Computing Biomedical Measurement And Imaging Blood Vessel Segmentation Blood Vessels CHASE_DB1 Databases Colour Retinal Image Computer Vision And Image Processing Technique Core Feature Set Digital Retinal Image Eye Feature Extraction Filtering Method in Signal Processing Frangi Vesselness Measure Gabor Filter Graph Theory Grey-level Co-occurrence Matrix Feature Health Physics Hu Moment Invariants Image Colour Analysis Image Filtering Image Recognition Image Segmentation Learning in AI Local Binary Pattern Feature Matrix Algebra Medical And Biomedical Uses of Field Medical Image Processing Neural Computing Technique Neural Network Training Patient Diagnostic Method And Instrumentation Radiations Radioactivity Retina Publicly Available Structured Analysis Retinal Blood Vessels Retinal Disease Diagnosis Retinal Vessel Network Retinal Vessel Segmentation Set Theory Supervised Segmentation Approach Vessel Extraction
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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