To boost the performance of level set algorithms, we propose the A-Levelset algorithm, which cascades the level set and active shape model (ASM). The A-Levelset-based ARGALI system is built to automatically segment the optic cup and optic disc from 2D digital fundus images. The ARGALI system further calculates the cup-to-disc ratio (CDR), which is an important indicator in glaucoma assessment and diagnosis. The ARGALI system was tested on a large clinical image collection of 2616 patients in order to estimate the CDR values. The extensive experimental results clearly show that ARGALI outperforms the level set-based approach by reducing the mean absolute error rate of CDR measurement from 0.349 to 0.21 and the mean square error rate from 0.156 to 0.07. ARGALI demonstrates for the first time the capability of automatic CDR measurement in a large clinical data set. It paves the way for automatic objective glaucoma diagnosis and screening using widely available fundus images.
ASJC Scopus subject areas
- General Engineering
- General Biochemistry,Genetics and Molecular Biology
- General Medicine