Publication result detail

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

BURGET, R.; UHER, V.; MAŠEK, J.

Original Title

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

English Title

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

Type

Paper in proceedings (conference paper)

Original Abstract

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.

English abstract

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.

Keywords

segmentation, data mining, image processing

Key words in English

segmentation, data mining, image processing

Authors

BURGET, R.; UHER, V.; MAŠEK, J.

RIV year

2013

Released

04.06.2012

Location

Barcelona

ISBN

978-1-4673-1118-2

Book

IEEE International Symposium on Biomedical Imaging

Edition

1

Pages from

17

Pages to

24

Pages count

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"
}