A review on automatic analysis techniques for color fundus photographs

Autor: Andras Hajdu, Renátó Besenczi, Janos Toth
Rok vydání: 2016
Předmět:
ROC
Retinopathy Online Challenge

Decision support system
SCC
Spearman's rank correlation coefficient

genetic structures
DR
diabetic retinopathy

SE
sensitivity

02 engineering and technology
Fundus (eye)
TN
true negative

Biochemistry
Field (computer science)
030218 nuclear medicine & medical imaging
Fundus image analysis
0302 clinical medicine
Biomedical imaging
Structural Biology
0202 electrical engineering
electronic engineering
information engineering

kNN
k-nearest neighbor

Medicine
Computer vision
AMD
age-related macular degeneration

FP
false positive

MA
microaneurysm

Clinical decision support
PPV
positive predictive value (precision)

RS
Retinopathy Online Challenge score

Retinal diseases
Computer Science Applications
FOV
field-of-view

020201 artificial intelligence & image processing
Biotechnology
NA
not available

lcsh:Biotechnology
Biophysics
ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
Future trend
Image processing
OD
optic disc

Clinical decision support system
03 medical and health sciences
lcsh:TP248.13-248.65
FN
false negative

Genetics
Medical imaging
ACC
accuracy

OC
optic cup

SP
specificity

business.industry
AUC
area under the receiver operator characteristics curve

Retinal image
eye diseases
FPI
false positive per image

TP
true positive

Short Survey
Artificial intelligence
sense organs
business
Zdroj: Computational and Structural Biotechnology Journal, Vol 14, Iss C, Pp 371-384 (2016)
Computational and Structural Biotechnology Journal
ISSN: 2001-0370
DOI: 10.1016/j.csbj.2016.10.001
Popis: In this paper, we give a review on automatic image processing tools to recognize diseases causing specific distortions in the human retina. After a brief summary of the biology of the retina, we give an overview of the types of lesions that may appear as biomarkers of both eye and non-eye diseases. We present several state-of-the-art procedures to extract the anatomic components and lesions in color fundus photographs and decision support methods to help clinical diagnosis. We list publicly available databases and appropriate measurement techniques to compare quantitatively the performance of these approaches. Furthermore, we discuss on how the performance of image processing-based systems can be improved by fusing the output of individual detector algorithms. Retinal image analysis using mobile phones is also addressed as an expected future trend in this field.
Databáze: OpenAIRE