Standard
Multiple classifier systems in texton-based approach for the classification of CT images of Lung. / Gangeh, Mehrdad J.; Sørensen, Lauge; Shaker, Saher B.; Kamel, Mohamed S.; de Bruijne, Marleen.
Medical Computer Vision. Recognition Techniques and Applications in Medical Imaging: International MICCAI Workshop, MCV 2010, Beijing, China, September 20, 2010, Revised Selected Papers. red. / Bjoern Menze; Georg Langs; Zhowen Tu; Antonio Criminsi. Springer, 2011. s. 153-163 (Lecture notes in computer science, Bind 6533).
Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt
Harvard
Gangeh, MJ, Sørensen, L, Shaker, SB, Kamel, MS
& de Bruijne, M 2011,
Multiple classifier systems in texton-based approach for the classification of CT images of Lung. i B Menze, G Langs, Z Tu & A Criminsi (red),
Medical Computer Vision. Recognition Techniques and Applications in Medical Imaging: International MICCAI Workshop, MCV 2010, Beijing, China, September 20, 2010, Revised Selected Papers. Springer, Lecture notes in computer science, bind 6533, s. 153-163, Medical Computer Vision 2010: Recognition Techniques and Applications in Medical Imaging, Beijing, Kina,
20/09/2010.
https://doi.org/10.1007/978-3-642-18421-5_15APA
Gangeh, M. J., Sørensen, L., Shaker, S. B., Kamel, M. S.
, & de Bruijne, M. (2011).
Multiple classifier systems in texton-based approach for the classification of CT images of Lung. I B. Menze, G. Langs, Z. Tu, & A. Criminsi (red.),
Medical Computer Vision. Recognition Techniques and Applications in Medical Imaging: International MICCAI Workshop, MCV 2010, Beijing, China, September 20, 2010, Revised Selected Papers (s. 153-163). Springer. Lecture notes in computer science Bind 6533
https://doi.org/10.1007/978-3-642-18421-5_15Vancouver
Gangeh MJ, Sørensen L, Shaker SB, Kamel MS
, de Bruijne M.
Multiple classifier systems in texton-based approach for the classification of CT images of Lung. I Menze B, Langs G, Tu Z, Criminsi A, red., Medical Computer Vision. Recognition Techniques and Applications in Medical Imaging: International MICCAI Workshop, MCV 2010, Beijing, China, September 20, 2010, Revised Selected Papers. Springer. 2011. s. 153-163. (Lecture notes in computer science, Bind 6533).
https://doi.org/10.1007/978-3-642-18421-5_15Author
Gangeh, Mehrdad J. ; Sørensen, Lauge ; Shaker, Saher B. ; Kamel, Mohamed S. ; de Bruijne, Marleen. / Multiple classifier systems in texton-based approach for the classification of CT images of Lung. Medical Computer Vision. Recognition Techniques and Applications in Medical Imaging: International MICCAI Workshop, MCV 2010, Beijing, China, September 20, 2010, Revised Selected Papers. red. / Bjoern Menze ; Georg Langs ; Zhowen Tu ; Antonio Criminsi. Springer, 2011. s. 153-163 (Lecture notes in computer science, Bind 6533).
Bibtex
@inproceedings{610e7a60902611df928f000ea68e967b,
title = "Multiple classifier systems in texton-based approach for the classification of CT images of Lung",
abstract = "In this paper, we propose using texton signatures based on raw pixelrepresentation along with a parallel multiple classifier system for the classification of emphysema in computed tomography images of the lung. The multiple classifier system is composed of support vector machines on the texton signatures as base classifiers and combines their decisions using product rule. The proposed approach is tested on 168 annotated regions of interest consisting of normal tissue, centrilobular emphysema, and paraseptal emphysema. Textonbased approach in texture classification mainly has two parameters, i.e., texton size and k value in k-means. Our results show that while aggregation of single decisions by SVMs over various k values using multiple classifier systems helps to improve the results compared to single SVMs, combining over differenttexton sizes is not beneficial. The performance of the proposed system, with anaccuracy of 95%, is similar to a recently proposed approach based on localbinary patterns, which performs almost the best among other approaches in theliterature.",
author = "Gangeh, {Mehrdad J.} and Lauge S{\o}rensen and Shaker, {Saher B.} and Kamel, {Mohamed S.} and {de Bruijne}, Marleen",
year = "2011",
doi = "10.1007/978-3-642-18421-5_15",
language = "English",
isbn = "978-3-642-18420-8",
series = "Lecture notes in computer science",
publisher = "Springer",
pages = "153--163",
editor = "Bjoern Menze and Georg Langs and Zhowen Tu and Antonio Criminsi",
booktitle = "Medical Computer Vision. Recognition Techniques and Applications in Medical Imaging",
address = "Switzerland",
note = "null ; Conference date: 20-09-2010 Through 20-09-2010",
}
RIS
TY - GEN
T1 - Multiple classifier systems in texton-based approach for the classification of CT images of Lung
AU - Gangeh, Mehrdad J.
AU - Sørensen, Lauge
AU - Shaker, Saher B.
AU - Kamel, Mohamed S.
AU - de Bruijne, Marleen
PY - 2011
Y1 - 2011
N2 - In this paper, we propose using texton signatures based on raw pixelrepresentation along with a parallel multiple classifier system for the classification of emphysema in computed tomography images of the lung. The multiple classifier system is composed of support vector machines on the texton signatures as base classifiers and combines their decisions using product rule. The proposed approach is tested on 168 annotated regions of interest consisting of normal tissue, centrilobular emphysema, and paraseptal emphysema. Textonbased approach in texture classification mainly has two parameters, i.e., texton size and k value in k-means. Our results show that while aggregation of single decisions by SVMs over various k values using multiple classifier systems helps to improve the results compared to single SVMs, combining over differenttexton sizes is not beneficial. The performance of the proposed system, with anaccuracy of 95%, is similar to a recently proposed approach based on localbinary patterns, which performs almost the best among other approaches in theliterature.
AB - In this paper, we propose using texton signatures based on raw pixelrepresentation along with a parallel multiple classifier system for the classification of emphysema in computed tomography images of the lung. The multiple classifier system is composed of support vector machines on the texton signatures as base classifiers and combines their decisions using product rule. The proposed approach is tested on 168 annotated regions of interest consisting of normal tissue, centrilobular emphysema, and paraseptal emphysema. Textonbased approach in texture classification mainly has two parameters, i.e., texton size and k value in k-means. Our results show that while aggregation of single decisions by SVMs over various k values using multiple classifier systems helps to improve the results compared to single SVMs, combining over differenttexton sizes is not beneficial. The performance of the proposed system, with anaccuracy of 95%, is similar to a recently proposed approach based on localbinary patterns, which performs almost the best among other approaches in theliterature.
U2 - 10.1007/978-3-642-18421-5_15
DO - 10.1007/978-3-642-18421-5_15
M3 - Article in proceedings
SN - 978-3-642-18420-8
T3 - Lecture notes in computer science
SP - 153
EP - 163
BT - Medical Computer Vision. Recognition Techniques and Applications in Medical Imaging
A2 - Menze, Bjoern
A2 - Langs, Georg
A2 - Tu, Zhowen
A2 - Criminsi, Antonio
PB - Springer
Y2 - 20 September 2010 through 20 September 2010
ER -
