Automatic Detection of Nerves in Confocal Corneal Images with Orientation-Based Edge Merging

Adam Brzeski

doi:10.1007/978-3-319-46589-0_5

Automatic Detection of Nerves in Confocal Corneal Images with Orientation-Based Edge Merging

Abstract

The paper presents an algorithm for improving results of automatic nerve detections in confocal microscopy images of human corneal. The method is designed as a postprocessing step of regular detection. After the nerves are initially detected, the algorithms attempts to improve the results by filling unde-sired gaps between single nerves detections in order to correctly mark the entire nerve instead of only parts of it. This approach enables for reliable detection of long nerves, which can be used for more accurate elimination of short detec-tions and therefore eliminating noise. The method evaluates candidate gaps by analysing the orientation of segments to be merged in the area near the gap. Segments with sufficiently high orientation compliance are merged to form a single nerve detection. Despite using only a simple technique for initial detec-tion of nerves, the method enabled achieving a fairly low ratio of wrong nerve detections with a balanced level of overall accuracy.

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Category:: Conference activity
Type:: publikacja w wydawnictwie zbiorowym recenzowanym (także w materiałach konferencyjnych)
Published in:: Advances in Intelligent Systems and Computing no. 523, pages 55 - 63,
ISSN: 2194-5357
Title of issue:: Information Systems Architecture and Technology: Proceedings of 37th International Conference on Information Systems Architecture and Technology – ISAT 2016 – Part III strony 55 - 63
Language:: English
Publication year:: 2016
Bibliographic description:: Brzeski A.: Automatic Detection of Nerves in Confocal Corneal Images with Orientation-Based Edge Merging// Information Systems Architecture and Technology: Proceedings of 37th International Conference on Information Systems Architecture and Technology – ISAT 2016 – Part III/ ed. Springer, Cham Polska: Springer International Publishing AG 2017, 2016, s.55-63
DOI:: Digital Object Identifier (open in new tab) 10.1007/978-3-319-46589-0_5
Verified by:: Gdańsk University of Technology