Publication details

Texture Analysis of 3D Fluorescence Microscopy Images Using RSurf 3D Features

Authors

STOKLASA Roman MAJTNER Tomáš

Type Article in Proceedings
Conference International Symposium on Biomedical Imaging (ISBI'16)
MU Faculty or unit

Faculty of Informatics

Citation
Doi http://dx.doi.org/10.1109/ISBI.2016.7493484
Field Use of computers, robotics and its application
Keywords RSurf features;HeLa cell images;object recognition;classification;fluorescence microscopy
Description Classification tasks of biomedical images are still interesting topic of research with many possibilities of improvement. A very important part in this task is feature extraction process, where different image descriptors are used. Recently, a new approach of RSurf features was introduced with application in recognition of the 2D HEp-2 cell images. In this work, we present the extension of these features for the 3D volumetric images and demonstrate its superiority in recognition of sub-cellular protein distribution. The performance is tested on public HeLa dataset containing 9 different classes. The presented k-NN classifier based purely on the RSurf 3D features achieves more than 99% accuracy in recognition of the 3D HeLa images.
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