A texton-based approach for the classification of lung parenchyma in CT images

Publikation: Bidrag til bog/antologi/rapportKonferencebidrag i proceedingsForskningfagfællebedømt

Standard

A texton-based approach for the classification of lung parenchyma in CT images. / Gangeh, Mehrdad J.; Sørensen, Lauge; Shaker, Saher B.; Kamel, Mohamed S.; de Bruijne, Marleen; Loog, Marco.

Medical Image Computing and Computer-Assisted Intervention - MICCAI 2010: 13th International Conference, Beijing, China, September 20-24, 2010, Proceedings, Part III. red. / Tianzi Jiang; Nassir Navab; Josien P. W. Pluim; Max A. Viergever. Bind Part III Springer, 2010. s. 595-602 (Lecture notes in computer science; Nr. 6363).

Publikation: Bidrag til bog/antologi/rapportKonferencebidrag i proceedingsForskningfagfællebedømt

Harvard

Gangeh, MJ, Sørensen, L, Shaker, SB, Kamel, MS, de Bruijne, M & Loog, M 2010, A texton-based approach for the classification of lung parenchyma in CT images. i T Jiang, N Navab, JPW Pluim & MA Viergever (red), Medical Image Computing and Computer-Assisted Intervention - MICCAI 2010: 13th International Conference, Beijing, China, September 20-24, 2010, Proceedings, Part III. bind Part III, Springer, Lecture notes in computer science, nr. 6363, s. 595-602, 13th International Conference on Medical Image Computing and Computer Assisted Intervention, Beijing, Kina, 20/09/2010. https://doi.org/10.1007/978-3-642-15711-0_74

APA

Gangeh, M. J., Sørensen, L., Shaker, S. B., Kamel, M. S., de Bruijne, M., & Loog, M. (2010). A texton-based approach for the classification of lung parenchyma in CT images. I T. Jiang, N. Navab, J. P. W. Pluim, & M. A. Viergever (red.), Medical Image Computing and Computer-Assisted Intervention - MICCAI 2010: 13th International Conference, Beijing, China, September 20-24, 2010, Proceedings, Part III (Bind Part III, s. 595-602). Springer. Lecture notes in computer science Nr. 6363 https://doi.org/10.1007/978-3-642-15711-0_74

Vancouver

Gangeh MJ, Sørensen L, Shaker SB, Kamel MS, de Bruijne M, Loog M. A texton-based approach for the classification of lung parenchyma in CT images. I Jiang T, Navab N, Pluim JPW, Viergever MA, red., Medical Image Computing and Computer-Assisted Intervention - MICCAI 2010: 13th International Conference, Beijing, China, September 20-24, 2010, Proceedings, Part III. Bind Part III. Springer. 2010. s. 595-602. (Lecture notes in computer science; Nr. 6363). https://doi.org/10.1007/978-3-642-15711-0_74

Author

Gangeh, Mehrdad J. ; Sørensen, Lauge ; Shaker, Saher B. ; Kamel, Mohamed S. ; de Bruijne, Marleen ; Loog, Marco. / A texton-based approach for the classification of lung parenchyma in CT images. Medical Image Computing and Computer-Assisted Intervention - MICCAI 2010: 13th International Conference, Beijing, China, September 20-24, 2010, Proceedings, Part III. red. / Tianzi Jiang ; Nassir Navab ; Josien P. W. Pluim ; Max A. Viergever. Bind Part III Springer, 2010. s. 595-602 (Lecture notes in computer science; Nr. 6363).

Bibtex

@inproceedings{831128f0651611df928f000ea68e967b,
title = "A texton-based approach for the classification of lung parenchyma in CT images",
abstract = "In this paper, a texton-based classification system based on raw pixel representation along with a support vector machine with radial basis function kernel is proposed for the classification of emphysema in computed tomography images of the lung. The proposed approach is tested on 168 annotated regions of interest consisting of normal tissue, centrilobular emphysema, and paraseptal emphysema. The results show the superiority of the proposed approach to common techniques in the literature including moments of the histogram of filter responses based on Gaussian derivatives. The performance of the proposed system, with an accuracy of 96.43%, also slightly improves over a recently proposed approach based on local binary patterns.",
author = "Gangeh, {Mehrdad J.} and Lauge S{\o}rensen and Shaker, {Saher B.} and Kamel, {Mohamed S.} and {de Bruijne}, Marleen and Marco Loog",
year = "2010",
doi = "10.1007/978-3-642-15711-0_74",
language = "English",
volume = "Part III",
series = "Lecture notes in computer science",
publisher = "Springer",
number = "6363",
pages = "595--602",
editor = "Tianzi Jiang and Nassir Navab and Pluim, {Josien P. W.} and Viergever, {Max A.}",
booktitle = "Medical Image Computing and Computer-Assisted Intervention - MICCAI 2010",
address = "Switzerland",
note = "null ; Conference date: 20-09-2010 Through 24-09-2010",

}

RIS

TY - GEN

T1 - A texton-based approach for the classification of lung parenchyma in CT images

AU - Gangeh, Mehrdad J.

AU - Sørensen, Lauge

AU - Shaker, Saher B.

AU - Kamel, Mohamed S.

AU - de Bruijne, Marleen

AU - Loog, Marco

N1 - Conference code: 13

PY - 2010

Y1 - 2010

N2 - In this paper, a texton-based classification system based on raw pixel representation along with a support vector machine with radial basis function kernel is proposed for the classification of emphysema in computed tomography images of the lung. The proposed approach is tested on 168 annotated regions of interest consisting of normal tissue, centrilobular emphysema, and paraseptal emphysema. The results show the superiority of the proposed approach to common techniques in the literature including moments of the histogram of filter responses based on Gaussian derivatives. The performance of the proposed system, with an accuracy of 96.43%, also slightly improves over a recently proposed approach based on local binary patterns.

AB - In this paper, a texton-based classification system based on raw pixel representation along with a support vector machine with radial basis function kernel is proposed for the classification of emphysema in computed tomography images of the lung. The proposed approach is tested on 168 annotated regions of interest consisting of normal tissue, centrilobular emphysema, and paraseptal emphysema. The results show the superiority of the proposed approach to common techniques in the literature including moments of the histogram of filter responses based on Gaussian derivatives. The performance of the proposed system, with an accuracy of 96.43%, also slightly improves over a recently proposed approach based on local binary patterns.

U2 - 10.1007/978-3-642-15711-0_74

DO - 10.1007/978-3-642-15711-0_74

M3 - Article in proceedings

VL - Part III

T3 - Lecture notes in computer science

SP - 595

EP - 602

BT - Medical Image Computing and Computer-Assisted Intervention - MICCAI 2010

A2 - Jiang, Tianzi

A2 - Navab, Nassir

A2 - Pluim, Josien P. W.

A2 - Viergever, Max A.

PB - Springer

Y2 - 20 September 2010 through 24 September 2010

ER -

ID: 19869956