Detail publikace

Trainable Segmentation Based on Local-level and Segment-level Feature Extraction

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"
}