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BURGET, R. UHER, V. MAŠEK, J.
Originální název
Trainable Segmentation Based on Local-level and Segment-level Feature Extraction
Typ
článek ve sborníku ve WoS nebo Scopus
Jazyk
angličtina
Originální abstrakt
This paper deals with the segmentation of neuronal struc- tures in electron microscope (EM) stacks, which is one of the challenges of the ISBI 2012 conference. The data for the challenge consists of a stack of 30 EM slices for training and 30 EM stacks for testing. The training data was labelled by an expert human neuroanatomist. In this paper a segmentation using local-level and segment-level features and machine learning algorithms was used. The results achieved on the ISBI 2012 challenge test set were: the Rand error: 0.139038440, warping er- ror: 0.002641296 and pixel error: 0.102285508. The main criterion for segmentation evaluation was the Rand error.
Klíčová slova
segmentation, data mining, image processing
Autoři
BURGET, R.; UHER, V.; MAŠEK, J.
Rok RIV
2012
Vydáno
4. 6. 2012
Místo
Barcelona
ISBN
978-1-4673-1118-2
Kniha
IEEE International Symposium on Biomedical Imaging
Edice
1
Číslo edice
2
Strany od
17
Strany do
24
Strany počet
63
BibTex
@inproceedings{BUT94573, author="Radim {Burget} and Václav {Uher} and Jan {Mašek}", title="Trainable Segmentation Based on Local-level and Segment-level Feature Extraction", booktitle="IEEE International Symposium on Biomedical Imaging", year="2012", series="1", number="2", pages="17--24", address="Barcelona", isbn="978-1-4673-1118-2" }