Ansatte – Københavns Universitet

Multiple classifier systems in texton-based approach for the classification of CT images of Lung

Publikation: Bidrag til bog/antologi/rapportKonferencebidrag i proceedingsForskningfagfællebedømt

Mehrdad J. Gangeh, Lauge Sørensen, Saher B. Shaker, Mohamed S. Kamel, Marleen de Bruijne

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. 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 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
TitelMedical Computer Vision. Recognition Techniques and Applications in Medical Imaging : International MICCAI Workshop, MCV 2010, Beijing, China, September 20, 2010, Revised Selected Papers
RedaktørerBjoern Menze, Georg Langs, Zhowen Tu, Antonio Criminsi
Antal sider11
ISBN (Trykt)978-3-642-18420-8
ISBN (Elektronisk)978-3-642-18421-5
StatusUdgivet - 2011
BegivenhedMedical Computer Vision 2010: Recognition Techniques and Applications in Medical Imaging - Beijing, Kina
Varighed: 20 sep. 201020 sep. 2010


KonferenceMedical Computer Vision 2010: Recognition Techniques and Applications in Medical Imaging
NavnLecture notes in computer science

ID: 170213099