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

Publikace nespadá pod Filozofickou fakultu, ale pod Fakultu informatiky. Oficiální stránka publikace je na webu muni.cz.



Rok publikování 2016
Druh Článek ve sborníku
Konference International Symposium on Biomedical Imaging (ISBI'16)
Fakulta / Pracoviště MU

Fakulta informatiky

Doi http://dx.doi.org/10.1109/ISBI.2016.7493484
Obor Využití počítačů, robotika a její aplikace
Klíčová slova RSurf features;HeLa cell images;object recognition;classification;fluorescence microscopy
Popis 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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