Abstract

To detect and identify viruses in electron microscopy images is crucial in certain clinical emergency situations. It is currently a highly manual task, requiring an expert sitting at the microscope to perform the analysis visually. Here we focus on and investigate one aspect towards automating the virus diagnostic task, namely recognizing the virus type based on their texture once possible virus objects have been segmented. We show that by using only local texture descriptors we achieve a classification rate of almost 89% on texture patches from 15 different virus types and a debris (false object) class. We compare and combine 5 different types of local texture descriptors and show that by combining the different types a lower classification error is achieved. We use a Random Forest Classifier and compare two approaches for feature selection.

Published in

Proceedings - International Conference On Pattern Recognition
2014, pages: 3227-3232
Book title: 2014 22nd International Conference on Pattern Recognition
eISBN: 978-1-4799-5208-3
Publisher: IEEE

Conference

22nd International Conference on Pattern Recognition (ICPR), AUG 24-28, 2014, Stockholm, SWEDEN

Authors' information