Delineation of blood vessels in pediatric retinal images using decision trees-based ensemble classification.
Int J Comput Assist Radiol Surg
; 9(5): 795-811, 2014 Sep.
Article
em En
| MEDLINE
| ID: mdl-24366332
ABSTRACT
PURPOSE:
Automatic segmentation of the retinal vasculature is a first step in computer-assisted diagnosis and treatment planning. The extraction of retinal vessels in pediatric retinal images is challenging because of comparatively wide arterioles with a light streak running longitudinally along the vessel's center, the central vessel reflex. A new method for automatic segmentation was developed and tested.METHOD:
A supervised method for retinal vessel segmentation in the images of multi-ethnic school children was developed based on ensemble classifier of bootstrapped decision trees. A collection of dual Gaussian, second derivative of Gaussian and Gabor filters, along with the generalized multiscale line strength measure and morphological transformation is used to generate the feature vector. The feature vector encodes information to handle the normal vessels as well as the vessels with the central reflex. The methodology is evaluated on CHASE_DB1, a relatively new public retinal image database of multi-ethnic school children, which is a subset of retinal images from the Child Heart and Health Study in England (CHASE) dataset.RESULTS:
The segmented retinal images from the CHASE_DB1 database produced best case accuracy, sensitivity and specificity of 0.96, 0.74 and 0.98, respectively, and worst case measures of 0.94, 0.67 and 0.98, respectively.CONCLUSION:
A new retinal blood vessel segmentation algorithm was developed and tested with a shared database. The observed accuracy, speed, robustness and simplicity suggest that the algorithm may be a suitable tool for automated retinal image analysis in large population-based studies.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Retina
/
Doenças Retinianas
/
Vasos Retinianos
/
Algoritmos
/
Reconhecimento Automatizado de Padrão
/
Árvores de Decisões
/
Diagnóstico por Computador
Tipo de estudo:
Diagnostic_studies
/
Health_economic_evaluation
/
Prognostic_studies
Limite:
Child
/
Humans
Idioma:
En
Revista:
Int J Comput Assist Radiol Surg
Assunto da revista:
RADIOLOGIA
Ano de publicação:
2014
Tipo de documento:
Article