Open AccessGradation of diabetic retinopathy on reconstructed image using compressed sensing
Author(s) -
Sil Kar Sudeshna,
Maity Santi P.
Publication year - 2018
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/iet-ipr.2017.1013
Subject(s) - gradation , diabetic retinopathy , artificial intelligence , tortuosity , computer science , computer vision , gaussian , compressed sensing , pattern recognition (psychology) , mathematics , medicine , materials science , physics , porosity , diabetes mellitus , endocrinology , quantum mechanics , composite material
This study explores neovascularisation and lesion detection in an integrated framework for gradation in diabetic retinopathy (DR). Imaging is assumed to be done from sub‐sample measurements following compressed sensing. Blind estimation of the scale of the matched filter (MF) followed by fuzzy entropy maximisation is done for extraction and classification of the thick and the thin vessels. Mutual information (MI) between vessel density and tortuosity of the thin vessel class is maximised in two dimensions (2D) for neovascularisation detection. For lesion detection, MI between the maximum MF response and the maximum Laplacian of Gaussian filter response is jointly maximised in 2D. The outcomes are then combined in a common platform for gradation in DR. Simulation results demonstrate that 95% images of each of DRIVE, STARE and DIARETDB1 databases and 94% images of MESSIDOR database are correctly graded by the proposed method when 80% measurement space is considered.