Abstract

We investigate the discriminant power of two local and two global texture measures on virus images. The viruses are imaged using negative stain transmission electron microscopy. Local binary patterns and a multi scale extension are compared to radial density profiles in the spatial domain and in the Fourier domain. To assess the discriminant potential of the texture measures a Random Forest classifier is used. Our analysis shows that the multi scale extension performs better than the standard local binary patterns and that radial density profiles in comparison is a rather poor virus texture discriminating measure. Furthermore, we show that the multi scale extension and the profiles in Fourier domain are both good texture measures and that they complement each other well, that is, they seem to detect different texture properties. Combining the two, hence, improves the discrimination between virus textures

Published in

Lecture Notes in Computer Science
2011, Volume: 7042, pages: 573-580
Title: Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
ISBN: 978-3-642-25084-2
Publisher: Springer

Conference

16th Iberoamerican Congress on Pattern Recognition

SLU Authors

• Sintorn, Ida-Maria

  • Centre for Image Analysis, Swedish University of Agricultural Sciences
    

UKÄ Subject classification

Computer Science
Medical Image Processing


Publication identifier

DOI: https://doi.org/10.1007/978-3-642-25085-9_68

Permanent link to this page (URI)

https://res.slu.se/id/publ/35991