Fuzzy-Cuts: A knowledge-driven graph-based method for medical image segmentation

D. R. Chittajallu, Gerd Brunner, U. Kurkure, R. P. Yalamanchili, I. A. Kakadiaris

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

13 Citations (Scopus)

Abstract

Image segmentation is, in general, an ill-posed problem and additional constraints need to be imposed in order to achieve the desired result. Particularly in the field of medical image segmentation, a significant amount of prior knowledge is available that can be used to constrain the solution space of the segmentation problem. However, most of this prior knowledge is, in general, vague or imprecise in nature, which makes it very difficult to model. This is the problem that is addressed in this paper. Specifically, in this paper, we present Fuzzy-Cuts, a novel, knowledge-driven, graph-based method for medical image segmentation. We cast the problem of image segmentation as the Maximum A Posteriori (MAP) estimation of a Markov Random Field (MRF) which, in essence, is equivalent to the minimization of the corresponding Gibbs energy function. Considering the inherent imprecision that is common in the a priori description of objects in medical images, we propose a fuzzy theoretic model to incorporate knowledge-driven constraints into the MAP-MRF formulation. In particular, we focus on prior information about the object's location, appearance and spatial connectivity to a known seed region inside the object. To that end, we introduce fuzzy connectivity and fuzzy location priors that are used in combination to define the first-order clique potential of the Gibbs energy function. In our experiments, we demonstrate the application of the proposed method to the challenging problem of heart segmentation in non-contrast computed tomography(CT) data.

Original languageEnglish (US)
Title of host publication2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops 2009
PublisherIEEE Computer Society
Pages715-722
Number of pages8
ISBN (Print)9781424439935
DOIs
StatePublished - Jan 1 2009
Event2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Miami, FL, United States
Duration: Jun 20 2009Jun 25 2009

Publication series

Name2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops 2009
Volume2009 IEEE Computer Society Conference on Computer Vision and ...

Other

Other2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition
CountryUnited States
CityMiami, FL
Period6/20/096/25/09

Fingerprint

Image segmentation
Gibbs free energy
Tomography
Seed
Experiments

All Science Journal Classification (ASJC) codes

  • Computer Vision and Pattern Recognition
  • Biomedical Engineering

Cite this

Chittajallu, D. R., Brunner, G., Kurkure, U., Yalamanchili, R. P., & Kakadiaris, I. A. (2009). Fuzzy-Cuts: A knowledge-driven graph-based method for medical image segmentation. In 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops 2009 (pp. 715-722). [5206623] (2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops 2009; Vol. 2009 IEEE Computer Society Conference on Computer Vision and ...). IEEE Computer Society. https://doi.org/10.1109/CVPRW.2009.5206623
Chittajallu, D. R. ; Brunner, Gerd ; Kurkure, U. ; Yalamanchili, R. P. ; Kakadiaris, I. A. / Fuzzy-Cuts : A knowledge-driven graph-based method for medical image segmentation. 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops 2009. IEEE Computer Society, 2009. pp. 715-722 (2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops 2009).
@inproceedings{dfb531d3377946c28ff9fa848d6e0a82,
title = "Fuzzy-Cuts: A knowledge-driven graph-based method for medical image segmentation",
abstract = "Image segmentation is, in general, an ill-posed problem and additional constraints need to be imposed in order to achieve the desired result. Particularly in the field of medical image segmentation, a significant amount of prior knowledge is available that can be used to constrain the solution space of the segmentation problem. However, most of this prior knowledge is, in general, vague or imprecise in nature, which makes it very difficult to model. This is the problem that is addressed in this paper. Specifically, in this paper, we present Fuzzy-Cuts, a novel, knowledge-driven, graph-based method for medical image segmentation. We cast the problem of image segmentation as the Maximum A Posteriori (MAP) estimation of a Markov Random Field (MRF) which, in essence, is equivalent to the minimization of the corresponding Gibbs energy function. Considering the inherent imprecision that is common in the a priori description of objects in medical images, we propose a fuzzy theoretic model to incorporate knowledge-driven constraints into the MAP-MRF formulation. In particular, we focus on prior information about the object's location, appearance and spatial connectivity to a known seed region inside the object. To that end, we introduce fuzzy connectivity and fuzzy location priors that are used in combination to define the first-order clique potential of the Gibbs energy function. In our experiments, we demonstrate the application of the proposed method to the challenging problem of heart segmentation in non-contrast computed tomography(CT) data.",
author = "Chittajallu, {D. R.} and Gerd Brunner and U. Kurkure and Yalamanchili, {R. P.} and Kakadiaris, {I. A.}",
year = "2009",
month = "1",
day = "1",
doi = "10.1109/CVPRW.2009.5206623",
language = "English (US)",
isbn = "9781424439935",
series = "2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops 2009",
publisher = "IEEE Computer Society",
pages = "715--722",
booktitle = "2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops 2009",
address = "United States",

}

Chittajallu, DR, Brunner, G, Kurkure, U, Yalamanchili, RP & Kakadiaris, IA 2009, Fuzzy-Cuts: A knowledge-driven graph-based method for medical image segmentation. in 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops 2009., 5206623, 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops 2009, vol. 2009 IEEE Computer Society Conference on Computer Vision and ..., IEEE Computer Society, pp. 715-722, 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Miami, FL, United States, 6/20/09. https://doi.org/10.1109/CVPRW.2009.5206623

Fuzzy-Cuts : A knowledge-driven graph-based method for medical image segmentation. / Chittajallu, D. R.; Brunner, Gerd; Kurkure, U.; Yalamanchili, R. P.; Kakadiaris, I. A.

2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops 2009. IEEE Computer Society, 2009. p. 715-722 5206623 (2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops 2009; Vol. 2009 IEEE Computer Society Conference on Computer Vision and ...).

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

TY - GEN

T1 - Fuzzy-Cuts

T2 - A knowledge-driven graph-based method for medical image segmentation

AU - Chittajallu, D. R.

AU - Brunner, Gerd

AU - Kurkure, U.

AU - Yalamanchili, R. P.

AU - Kakadiaris, I. A.

PY - 2009/1/1

Y1 - 2009/1/1

N2 - Image segmentation is, in general, an ill-posed problem and additional constraints need to be imposed in order to achieve the desired result. Particularly in the field of medical image segmentation, a significant amount of prior knowledge is available that can be used to constrain the solution space of the segmentation problem. However, most of this prior knowledge is, in general, vague or imprecise in nature, which makes it very difficult to model. This is the problem that is addressed in this paper. Specifically, in this paper, we present Fuzzy-Cuts, a novel, knowledge-driven, graph-based method for medical image segmentation. We cast the problem of image segmentation as the Maximum A Posteriori (MAP) estimation of a Markov Random Field (MRF) which, in essence, is equivalent to the minimization of the corresponding Gibbs energy function. Considering the inherent imprecision that is common in the a priori description of objects in medical images, we propose a fuzzy theoretic model to incorporate knowledge-driven constraints into the MAP-MRF formulation. In particular, we focus on prior information about the object's location, appearance and spatial connectivity to a known seed region inside the object. To that end, we introduce fuzzy connectivity and fuzzy location priors that are used in combination to define the first-order clique potential of the Gibbs energy function. In our experiments, we demonstrate the application of the proposed method to the challenging problem of heart segmentation in non-contrast computed tomography(CT) data.

AB - Image segmentation is, in general, an ill-posed problem and additional constraints need to be imposed in order to achieve the desired result. Particularly in the field of medical image segmentation, a significant amount of prior knowledge is available that can be used to constrain the solution space of the segmentation problem. However, most of this prior knowledge is, in general, vague or imprecise in nature, which makes it very difficult to model. This is the problem that is addressed in this paper. Specifically, in this paper, we present Fuzzy-Cuts, a novel, knowledge-driven, graph-based method for medical image segmentation. We cast the problem of image segmentation as the Maximum A Posteriori (MAP) estimation of a Markov Random Field (MRF) which, in essence, is equivalent to the minimization of the corresponding Gibbs energy function. Considering the inherent imprecision that is common in the a priori description of objects in medical images, we propose a fuzzy theoretic model to incorporate knowledge-driven constraints into the MAP-MRF formulation. In particular, we focus on prior information about the object's location, appearance and spatial connectivity to a known seed region inside the object. To that end, we introduce fuzzy connectivity and fuzzy location priors that are used in combination to define the first-order clique potential of the Gibbs energy function. In our experiments, we demonstrate the application of the proposed method to the challenging problem of heart segmentation in non-contrast computed tomography(CT) data.

UR - http://www.scopus.com/inward/record.url?scp=70450162141&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=70450162141&partnerID=8YFLogxK

U2 - 10.1109/CVPRW.2009.5206623

DO - 10.1109/CVPRW.2009.5206623

M3 - Conference contribution

AN - SCOPUS:70450162141

SN - 9781424439935

T3 - 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops 2009

SP - 715

EP - 722

BT - 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops 2009

PB - IEEE Computer Society

ER -

Chittajallu DR, Brunner G, Kurkure U, Yalamanchili RP, Kakadiaris IA. Fuzzy-Cuts: A knowledge-driven graph-based method for medical image segmentation. In 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops 2009. IEEE Computer Society. 2009. p. 715-722. 5206623. (2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops 2009). https://doi.org/10.1109/CVPRW.2009.5206623