Authors:
Tudor-Ionut Nedelcu
;
Francisco Veiga
;
Miguel Santos
;
Marcos Liberal
and
Filipe Soares
Affiliation:
Fraunhofer Portugal AICOS, Rua Alfredo Allen 455/461, 4200-135 Porto, Portugal
Keyword(s):
Retina, Smartphone, Image Acquisition, Autofocus, Feature Extraction, Machine Learning.
Abstract:
Mobile fundus imaging devices can play an important role in the decentralization of eye diseases screening methods, increasing the accessibility of telemedicine solutions in this area. Since image focusing is crucial to obtain an optimal retinal image, this work presents a smartphone-based approach for automatic focus assessment of NIR retinal images, acquired by a prototype of a handheld fundus camera device called EyeFundusScope (EFS) A009. A DCT-based focus metric is proposed and compared against a group of Gradient-based, Statistical-based, and Laplacian-based functions in the same experimental setup. The paper also presents the EFS image acquisition logic and the protocol for creating the necessary NIR dataset with the optic disc region around the centre of the image. The results were obtained within 853 images acquired from 8 volunteers. The developed method combined with other features, and a SVM classifier in a Machine Learning approach which attained an AUC of 0.80, has show
n to be a viable solution to integrate into the EFS mobile application.
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