Multi-region labeling and segmentation using a graph topology prior and atlas information in brain images

Research output: Contribution to journalArticleResearchpeer review

Authors

Research Organisations

View graph of relations

Details

Original languageEnglish
Pages (from-to)725-734
Number of pages10
JournalComputerized Medical Imaging and Graphics
Volume38
Issue number8
Publication statusPublished - 23 Jun 2014

Abstract

Medical image segmentation and anatomical structure labeling according to the types of the tissues are important for accurate diagnosis and therapy. In this paper, we propose a novel approach for multi-region labeling and segmentation, which is based on a topological graph prior and the topological information of an atlas, using a modified multi-level set energy minimization method in brain images. We consider a topological graph prior and atlas information to evolve the contour based on a topological relationship presented via a graph relation. This novel method is capable of segmenting adjacent objects with very close gray level in low resolution brain image that would be difficult to segment correctly using standard methods. The topological information of an atlas are transformed to the topological graph of a low resolution (noisy) brain image to obtain region labeling. We explain our algorithm and show the topological graph prior and label transformation techniques to explain how it gives precise multi-region segmentation and labeling. The proposed algorithm is capable of segmenting and labeling different regions in noisy or low resolution MRI brain images of different modalities. We compare our approaches with other state-of-the-art approaches for multi-region labeling and segmentation.

Keywords

    Atlas information, Labeling, Medical image, Multi-level set, Multi-region, Segmentation, Topological graph

ASJC Scopus subject areas

Cite this

Multi-region labeling and segmentation using a graph topology prior and atlas information in brain images. / Al-Shaikhli, Saif Dawood Salman; Yang, Michael Ying; Rosenhahn, Bodo.
In: Computerized Medical Imaging and Graphics, Vol. 38, No. 8, 23.06.2014, p. 725-734.

Research output: Contribution to journalArticleResearchpeer review

Download
@article{872c476c485341beaf44d10c84771a08,
title = "Multi-region labeling and segmentation using a graph topology prior and atlas information in brain images",
abstract = "Medical image segmentation and anatomical structure labeling according to the types of the tissues are important for accurate diagnosis and therapy. In this paper, we propose a novel approach for multi-region labeling and segmentation, which is based on a topological graph prior and the topological information of an atlas, using a modified multi-level set energy minimization method in brain images. We consider a topological graph prior and atlas information to evolve the contour based on a topological relationship presented via a graph relation. This novel method is capable of segmenting adjacent objects with very close gray level in low resolution brain image that would be difficult to segment correctly using standard methods. The topological information of an atlas are transformed to the topological graph of a low resolution (noisy) brain image to obtain region labeling. We explain our algorithm and show the topological graph prior and label transformation techniques to explain how it gives precise multi-region segmentation and labeling. The proposed algorithm is capable of segmenting and labeling different regions in noisy or low resolution MRI brain images of different modalities. We compare our approaches with other state-of-the-art approaches for multi-region labeling and segmentation.",
keywords = "Atlas information, Labeling, Medical image, Multi-level set, Multi-region, Segmentation, Topological graph",
author = "Al-Shaikhli, {Saif Dawood Salman} and Yang, {Michael Ying} and Bodo Rosenhahn",
note = "Funding information: The work was partially funded by DAAD scholarship (A/10/96106) and MOHESR-Iraq (Baghdad University). The authors gratefully acknowledge these supports.",
year = "2014",
month = jun,
day = "23",
doi = "10.1016/j.compmedimag.2014.06.008",
language = "English",
volume = "38",
pages = "725--734",
journal = "Computerized Medical Imaging and Graphics",
issn = "0895-6111",
publisher = "Elsevier Ltd.",
number = "8",

}

Download

TY - JOUR

T1 - Multi-region labeling and segmentation using a graph topology prior and atlas information in brain images

AU - Al-Shaikhli, Saif Dawood Salman

AU - Yang, Michael Ying

AU - Rosenhahn, Bodo

N1 - Funding information: The work was partially funded by DAAD scholarship (A/10/96106) and MOHESR-Iraq (Baghdad University). The authors gratefully acknowledge these supports.

PY - 2014/6/23

Y1 - 2014/6/23

N2 - Medical image segmentation and anatomical structure labeling according to the types of the tissues are important for accurate diagnosis and therapy. In this paper, we propose a novel approach for multi-region labeling and segmentation, which is based on a topological graph prior and the topological information of an atlas, using a modified multi-level set energy minimization method in brain images. We consider a topological graph prior and atlas information to evolve the contour based on a topological relationship presented via a graph relation. This novel method is capable of segmenting adjacent objects with very close gray level in low resolution brain image that would be difficult to segment correctly using standard methods. The topological information of an atlas are transformed to the topological graph of a low resolution (noisy) brain image to obtain region labeling. We explain our algorithm and show the topological graph prior and label transformation techniques to explain how it gives precise multi-region segmentation and labeling. The proposed algorithm is capable of segmenting and labeling different regions in noisy or low resolution MRI brain images of different modalities. We compare our approaches with other state-of-the-art approaches for multi-region labeling and segmentation.

AB - Medical image segmentation and anatomical structure labeling according to the types of the tissues are important for accurate diagnosis and therapy. In this paper, we propose a novel approach for multi-region labeling and segmentation, which is based on a topological graph prior and the topological information of an atlas, using a modified multi-level set energy minimization method in brain images. We consider a topological graph prior and atlas information to evolve the contour based on a topological relationship presented via a graph relation. This novel method is capable of segmenting adjacent objects with very close gray level in low resolution brain image that would be difficult to segment correctly using standard methods. The topological information of an atlas are transformed to the topological graph of a low resolution (noisy) brain image to obtain region labeling. We explain our algorithm and show the topological graph prior and label transformation techniques to explain how it gives precise multi-region segmentation and labeling. The proposed algorithm is capable of segmenting and labeling different regions in noisy or low resolution MRI brain images of different modalities. We compare our approaches with other state-of-the-art approaches for multi-region labeling and segmentation.

KW - Atlas information

KW - Labeling

KW - Medical image

KW - Multi-level set

KW - Multi-region

KW - Segmentation

KW - Topological graph

UR - http://www.scopus.com/inward/record.url?scp=84911991809&partnerID=8YFLogxK

U2 - 10.1016/j.compmedimag.2014.06.008

DO - 10.1016/j.compmedimag.2014.06.008

M3 - Article

C2 - 24998760

AN - SCOPUS:84911991809

VL - 38

SP - 725

EP - 734

JO - Computerized Medical Imaging and Graphics

JF - Computerized Medical Imaging and Graphics

SN - 0895-6111

IS - 8

ER -

By the same author(s)