Open AccessNo-Reference Retinal Image Sharpness Metric Using Daubechies Wavelet Transform
Author(s) -
Preecha Vonghirandecha,
Patama Bhurayatachai,
Supaporn Kansomkeat,
S. Intajag
Publication year - 2021
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 - 10.46300/9106.2021.15.115
Subject(s) - artificial intelligence , wavelet , computer science , metric (unit) , computer vision , wavelet transform , pattern recognition (psychology) , daubechies wavelet , image quality , fundus (uterus) , image (mathematics) , discrete wavelet transform , medicine , radiology , operations management , economics
Retinal fundus images are increasingly used by ophthalmologists both manually and without human intervention for detecting ocular diseases. Poor quality retinal images could lead to misdiagnosis or delayed treatment. Hence, a picture quality index was a crucial measure to ensure that the obtained images from acquisition system were suitable for reliable medical diagnosis. In this paper, a no-reference retinal image quality assessment based on wavelet transform is presented. A multiresolution Daubechies (db2) wavelet at level 4 was employed to decompose an original image into detail, and approximation sub-bands for extracting the sharpness information. The sharpness quality index was calculated from the entropy of the sub-bands. The proposed measure was validated by using images from the High-Resolution Fundus (HRF) dataset. The experimental results show that the proposed index performed more consistent with human visual perception and outperformed the Abdel-Hamid et al method.