Open AccessAutomatic production of synthetic labelled OCT images using an active shape model
Author(s) -
Danesh Hajar,
Maghooli Keivan,
Dehghani Alireza,
Kafieh Rahele
Publication year - 2020
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.2020.0075
Subject(s) - computer science , artificial intelligence , optical coherence tomography , speckle noise , computer vision , speckle pattern , image processing , noise (video) , shadow (psychology) , brightness , synthetic data , pattern recognition (psychology) , field (mathematics) , algorithm , image (mathematics) , mathematics , optics , psychology , physics , pure mathematics , psychotherapist
Limited labelled data is a challenge in the field of medical imaging and the need for a large number of them is paramount for the training of machine learning algorithms, as well as measuring the performance of image processing algorithms. The purpose of this study is to construct synthetic and labelled optical coherence tomography (OCT) data to solve the problems of having access to accurately labelled data and evaluating the processing algorithms. In this study, a modified active shape model is used which considers the anatomical features of available images such as the number and thickness of the layers as well as their associated brightness, the location of retinal blood vessels and shadow information with respect to speckle noise. The algorithm is also able to provide different data sets with the varying noise level. The validity of the proposed method for the synthesis of retinal images is measured by two methods (qualitative assessment and quantitative analysis).