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Springer, Lecture Notes in Computer Science, p. 181-188, 2010

DOI: 10.1007/978-3-642-15711-0_23

Springer, Lecture Notes in Computer Science, p. 37-44, 2010

DOI: 10.1007/978-3-642-15705-9_5

Springer, Lecture Notes in Computer Science, p. 617-624, 2009

DOI: 10.1007/978-3-642-04271-3_75

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Improved segmentation of focal cortical dysplasia lesions on MRI using expansion towards cortical boundaries

Journal article published in 2006 by Tardif Cl, Philippe Zysset, Marleen de Bruijne ORCID, Saher B. Shaker, Eskildsen Sf, Christine L. Tardif, Lauge Emil Borch Laurs Sørensen, Martha E. Shenton, Carl-Fredrik Westin, Jon Sporring, John B. Richardson, G. Bruce Pike, Mert R. Sabuncu, Hae-Jeong Park, David W. Roberts and other authors.
This paper is made freely available by the publisher.
This paper is made freely available by the publisher.

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Abstract

In this paper, we propose to classify medical images using dissimilarities computed between collections of regions of interest. The images are mapped into a dissimilarity space using an image dissimilarity measure, and a standard vector space-based classifier is applied in this space. The classification output of this approach can be used in computer aided-diagnosis problems where the goal is to detect the presence of abnormal regions or to quantify the extent or severity of abnormalities in these regions. The proposed approach is applied to quantify chronic obstructive pulmonary disease in computed tomography (CT) images, achieving an area under the receiver operating characteristic curve of 0.817. This is significantly better compared to combining individual region classifications into an overall image classification, and compared to common computerized quantitative measures in pulmonary CT. ; In this paper, we propose to classify medical images using dissimilarities computed between collections of regions of interest. The images are mapped into a dissimilarity space using an image dissimilarity measure, and a standard vector space-based classifier is applied in this space. The classification output of this approach can be used in computer aided-diagnosis problems where the goal is to detect the presence of abnormal regions or to quantify the extent or severity of abnormalities in these regions. The proposed approach is applied to quantify chronic obstructive pulmonary disease in computed tomography (CT) images, achieving an area under the receiver operating characteristic curve of 0.817. This is significantly better compared to combining individual region classifications into an overall image classification, and compared to common computerized quantitative measures in pulmonary CT.