Open AccessRetinal Image Enhancement using Ordering Gap Adjustment and Brightness Specification
Publication year - 2020
Publication title -
international journal of circuits, systems and signal processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.156
H-Index - 13
ISSN - 1998-4464
DOI - 10.46300/9106.2020.14.56
Subject(s) - adaptive histogram equalization , brightness , histogram equalization , computer science , image histogram , histogram matching , histogram , artificial intelligence , computer vision , color normalization , algorithm , image (mathematics) , color histogram , color image , mathematics , pattern recognition (psychology) , image processing , optics , physics
Color retinal image enhancement plays an important role in improving an image quality suited for reliable diagnosis. For this problem domain, a simple and effective algorithm for image contrast and color balance enhancement namely Ordering Gap Adjustment and Brightness Specification (OGABS) was proposed. The OGABS algorithm first constructs a specified histogram by adjusting the gap of the input image histogram ordering by its probability density function under gap limiter and Hubbard’s dynamic range specifications. Then, the specified histograms are targets to redistribute the intensity values of the input image based on histogram matching. Finally, color balance is improved by specifying the image brightness based on Hubbard’s brightness specification. The OGABS algorithm is implemented by the MATLAB program and the performance of our algorithm has been evaluated against data from STARE and DiaretDB0 datasets. The results obtained show that our algorithm enhances the image contrast and creates a good color balance in a pleasing natural appearance with a standard color of lesions.