Open AccessRetinal Image Enhancement Using Robust Inverse Diffusion Equation and Self-Similarity Filtering
Author(s) -
Lu Wang,
Guohua Li,
Shujun Fu,
Lingzhong Xu,
Kaixuan Zhao,
Caiming Zhang
Publication year - 2016
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0158480
Subject(s) - artificial intelligence , anisotropic diffusion , computer science , diabetic retinopathy , similarity (geometry) , retinal , pattern recognition (psychology) , computer vision , image processing , diffusion equation , feature (linguistics) , image (mathematics) , medicine , ophthalmology , metric (unit) , diabetes mellitus , operations management , economics , endocrinology , linguistics , philosophy
As a common ocular complication for diabetic patients, diabetic retinopathy has become an important public health problem in the world. Early diagnosis and early treatment with the help of fundus imaging technology is an effective control method. In this paper, a robust inverse diffusion equation combining a self-similarity filtering is presented to detect and evaluate diabetic retinopathy using retinal image enhancement. A flux corrected transport technique is used to control diffusion flux adaptively, which eliminates overshoots inherent in the Laplacian operation. Feature preserving denoising by the self-similarity filtering ensures a robust enhancement of noisy and blurry retinal images. Experimental results demonstrate that this algorithm can enhance important details of retinal image data effectively, affording an opportunity for better medical interpretation and subsequent processing.