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.

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STOKLASA Roman MAJTNER Tomáš

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

Fakulta informatiky

Citace
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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