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
N1 - Funding Information: The funding from the Natural Sciences and Engineering Research Council (NSERC) of Canada under Canada Graduate Scholarship (CGS D3-378361-2009) and Michael Smith Foreign Study Supplements (MSFSS) is gratefully acknowledged.
PY - 2010
Y1 - 2010
N2 - In this paper, we propose using texton signatures based on raw pixel representation 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. Texton-based 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 different texton sizes is not beneficial. The performance of the proposed system, with an accuracy of 95%, is similar to a recently proposed approach based on local binary patterns, which performs almost the best among other approaches in the literature.
AB - In this paper, we propose using texton signatures based on raw pixel representation 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. Texton-based 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 different texton sizes is not beneficial. The performance of the proposed system, with an accuracy of 95%, is similar to a recently proposed approach based on local binary patterns, which performs almost the best among other approaches in the literature.
UR - http://www.scopus.com/inward/record.url?scp=79951598366&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-18421-5_15
DO - 10.1007/978-3-642-18421-5_15
M3 - Conference proceeding
AN - SCOPUS:79951598366
SN - 9783642184208
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 153
EP - 163
BT - Medical Computer Vision
T2 - Workshop on Medical Computer Vision, MCV 2010, Held in Conjunction with the 13th International Conference on Medical Image Computing and Computer - Assisted Intervention, MICCAI 2010
Y2 - 20 September 2010 through 20 September 2010
ER -