A variational mode decomposition (VMD) and local binary patterns (LBP) based features extraction from digital fundus images is proposed for glaucoma detection. The band-limited intrinsic mode images (BLIM’s) obtained by VMD, encompasses the varying spectral content embodying the non-linear and spatial non-stationary textural modulations in the fundus images. LBP feature descriptors apprehend the topographic tortuousness of the optical tissue fluids and substantiate the perturbations in intraocular fluid pressure (IOP) within the human eye which is caused due to glitches in the optical drainage system. Using artificial neural network, a classification accuracy of 95.2% is obtained on publicly available Medical Image Analysis Group (MIAG) dataset, which validates the suitability of the proposed framework in glaucoma identification.

Date Published: 24 May 2018
PDF: 5 pages
Proc. SPIE 10677, Unconventional Optical Imaging, 106772Y (24 May 2018); doi: 10.1117/12.2291996

Published in SPIE Proceedings Vol. 10677:
Unconventional Optical Imaging
Corinne Fournier; Marc P. Georges; Gabriel Popescu, Editor(s)