A Novel Adaptive Deformable Model for Automated Optic Disc and Cup Segmentation to Aid Glaucoma Diagnosis

Autor: Louis R. Pasquale, Baihua Li, Jano van Hemert, Alan Fleming, Liangxiu Han, Muhammad Salman Haleem, Brian J. Song
Rok vydání: 2017
Předmět:
Computer science
Feature vector
Optic Disk
Optic disk
Medicine (miscellaneous)
Glaucoma
Image & Signal Processing
Health Informatics
Image processing
02 engineering and technology
Pattern Recognition
Automated
Computer-aided retinal disease diagnosis
Machine Learning
03 medical and health sciences
0302 clinical medicine
Health Information Management
Robustness (computer science)
Image Processing
Computer-Assisted
0202 electrical engineering
electronic engineering
information engineering
medicine
Humans
Segmentation
Computer vision
business.industry
medicine.disease
Medical image processing and analysis
medicine.anatomical_structure
030221 ophthalmology & optometry
020201 artificial intelligence & image processing
Artificial intelligence
business
Algorithms
Smoothing
Information Systems
Optic disc
Zdroj: Journal of Medical Systems
ISSN: 1573-689X
0148-5598
DOI: 10.1007/s10916-017-0859-4
Popis: This paper proposes a novel Adaptive Region based Edge Smoothing Model (ARESM) for automatic boundary detection of optic disc and cup to aid automatic glaucoma diagnosis. The novelty of our approach consists of two aspects: 1) automatic detection of initial optimum object boundary based on a Region Classifi- cation Model (RCM) in a pixel-level multidimensional feature space; 2) an Adaptive Edge Smoothing Update model (AESU) of contour points (e.g. misclassified or irregular points) based on iterative force field calculations with contours obtained from the RCM model by minimising energy function (an approach that does not require predefined geometric templates to guide autosegmentation). Such an approach provides robustness in capturing a range of variations and shapes. We have conducted a comprehensive comparison between our approach and the state-of-the-art existing deformable models and validated it with publicly available datasets. The experimental evaluation shows that the proposed approach significantly outperforms existing methods. The generality of the proposed approach will enable segmentation and detection of other object boundaries and provide added value in the field of medical image processing and analysis.
Databáze: OpenAIRE
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