Multiple atlases-based joint labeling of human cortical sulcal curves

Ilwoo Lyu, Gang Li, Minjeong Kim, Dinggang Shen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

We present a spectral-based sulcal curve labeling method by considering geometrical information of neighboring curves in a multiple atlases-based framework. Compared to the conventional method, we propose to use neighboring curves for avoiding ambiguity in curve-by-curve labeling and to integrate the labeling results obtained from multiple atlases for consistent labeling. In particular, we compute a histogram of points on the neighboring curves as a new feature descriptor for each point on a sulcal curve under consideration. To better resolve ambiguity in the curve labeling, we also employ the neighboring curves that are parallel to major sulcal curves. Moreover, we further integrate all the results from multiple atlases into a linear system, by solving which our method ultimately gives accurate labels to the major curves in the subjects. Experimental results on evaluation of 12 major sulcal curves of 12 human cortical surfaces indicate that our method achieves higher labeling accuracy 7.87% compared to the conventional method, while reducing 4.41% of false positive labeling errors on average.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages124-132
Number of pages9
Volume7766 LNCS
DOIs
Publication statusPublished - 2013 Mar 25
Externally publishedYes
Event2nd MICCAI Workshop on Medical Computer Vision, MICCAI-MCV 2012, Held in Conjunction with the 15th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2012 - Nice, France
Duration: 2012 Oct 52012 Oct 5

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7766 LNCS
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other2nd MICCAI Workshop on Medical Computer Vision, MICCAI-MCV 2012, Held in Conjunction with the 15th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2012
CountryFrance
CityNice
Period12/10/512/10/5

Fingerprint

Atlas
Labeling
Curve
Human
Linear systems
Labels
Integrate
False Positive
Histogram
Descriptors
Resolve
Linear Systems

Keywords

  • multiple atlases
  • spectral matching
  • sulcal curve labeling

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

Cite this

Lyu, I., Li, G., Kim, M., & Shen, D. (2013). Multiple atlases-based joint labeling of human cortical sulcal curves. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7766 LNCS, pp. 124-132). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7766 LNCS). https://doi.org/10.1007/978-3-642-36620-8_13

Multiple atlases-based joint labeling of human cortical sulcal curves. / Lyu, Ilwoo; Li, Gang; Kim, Minjeong; Shen, Dinggang.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 7766 LNCS 2013. p. 124-132 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7766 LNCS).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Lyu, I, Li, G, Kim, M & Shen, D 2013, Multiple atlases-based joint labeling of human cortical sulcal curves. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). vol. 7766 LNCS, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 7766 LNCS, pp. 124-132, 2nd MICCAI Workshop on Medical Computer Vision, MICCAI-MCV 2012, Held in Conjunction with the 15th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2012, Nice, France, 12/10/5. https://doi.org/10.1007/978-3-642-36620-8_13
Lyu I, Li G, Kim M, Shen D. Multiple atlases-based joint labeling of human cortical sulcal curves. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 7766 LNCS. 2013. p. 124-132. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-642-36620-8_13
Lyu, Ilwoo ; Li, Gang ; Kim, Minjeong ; Shen, Dinggang. / Multiple atlases-based joint labeling of human cortical sulcal curves. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 7766 LNCS 2013. pp. 124-132 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
@inproceedings{0e3a7c119f124daeb05d59c4c955bc57,
title = "Multiple atlases-based joint labeling of human cortical sulcal curves",
abstract = "We present a spectral-based sulcal curve labeling method by considering geometrical information of neighboring curves in a multiple atlases-based framework. Compared to the conventional method, we propose to use neighboring curves for avoiding ambiguity in curve-by-curve labeling and to integrate the labeling results obtained from multiple atlases for consistent labeling. In particular, we compute a histogram of points on the neighboring curves as a new feature descriptor for each point on a sulcal curve under consideration. To better resolve ambiguity in the curve labeling, we also employ the neighboring curves that are parallel to major sulcal curves. Moreover, we further integrate all the results from multiple atlases into a linear system, by solving which our method ultimately gives accurate labels to the major curves in the subjects. Experimental results on evaluation of 12 major sulcal curves of 12 human cortical surfaces indicate that our method achieves higher labeling accuracy 7.87{\%} compared to the conventional method, while reducing 4.41{\%} of false positive labeling errors on average.",
keywords = "multiple atlases, spectral matching, sulcal curve labeling",
author = "Ilwoo Lyu and Gang Li and Minjeong Kim and Dinggang Shen",
year = "2013",
month = "3",
day = "25",
doi = "10.1007/978-3-642-36620-8_13",
language = "English",
isbn = "9783642366192",
volume = "7766 LNCS",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
pages = "124--132",
booktitle = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

}

TY - GEN

T1 - Multiple atlases-based joint labeling of human cortical sulcal curves

AU - Lyu, Ilwoo

AU - Li, Gang

AU - Kim, Minjeong

AU - Shen, Dinggang

PY - 2013/3/25

Y1 - 2013/3/25

N2 - We present a spectral-based sulcal curve labeling method by considering geometrical information of neighboring curves in a multiple atlases-based framework. Compared to the conventional method, we propose to use neighboring curves for avoiding ambiguity in curve-by-curve labeling and to integrate the labeling results obtained from multiple atlases for consistent labeling. In particular, we compute a histogram of points on the neighboring curves as a new feature descriptor for each point on a sulcal curve under consideration. To better resolve ambiguity in the curve labeling, we also employ the neighboring curves that are parallel to major sulcal curves. Moreover, we further integrate all the results from multiple atlases into a linear system, by solving which our method ultimately gives accurate labels to the major curves in the subjects. Experimental results on evaluation of 12 major sulcal curves of 12 human cortical surfaces indicate that our method achieves higher labeling accuracy 7.87% compared to the conventional method, while reducing 4.41% of false positive labeling errors on average.

AB - We present a spectral-based sulcal curve labeling method by considering geometrical information of neighboring curves in a multiple atlases-based framework. Compared to the conventional method, we propose to use neighboring curves for avoiding ambiguity in curve-by-curve labeling and to integrate the labeling results obtained from multiple atlases for consistent labeling. In particular, we compute a histogram of points on the neighboring curves as a new feature descriptor for each point on a sulcal curve under consideration. To better resolve ambiguity in the curve labeling, we also employ the neighboring curves that are parallel to major sulcal curves. Moreover, we further integrate all the results from multiple atlases into a linear system, by solving which our method ultimately gives accurate labels to the major curves in the subjects. Experimental results on evaluation of 12 major sulcal curves of 12 human cortical surfaces indicate that our method achieves higher labeling accuracy 7.87% compared to the conventional method, while reducing 4.41% of false positive labeling errors on average.

KW - multiple atlases

KW - spectral matching

KW - sulcal curve labeling

UR - http://www.scopus.com/inward/record.url?scp=84875139551&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84875139551&partnerID=8YFLogxK

U2 - 10.1007/978-3-642-36620-8_13

DO - 10.1007/978-3-642-36620-8_13

M3 - Conference contribution

SN - 9783642366192

VL - 7766 LNCS

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 124

EP - 132

BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

ER -