Combining binary classifiers for automatic cartilage segmentation in knee MRI

Authors:
Jenny Folkesson;Ole Fogh Olsen;Paola Pettersen;Erik Dam;Claus Christiansen
Affiliations:
Image Analysis Group, IT University of Copenhagen, Denmark;Image Analysis Group, IT University of Copenhagen, Denmark;Center for Clinical and Basic Research, Ballerup, Denmark;Image Analysis Group, IT University of Copenhagen, Denmark;Center for Clinical and Basic Research, Ballerup, Denmark
Venue:
CVBIA'05 Proceedings of the First international conference on Computer Vision for Biomedical Image Applications
Year:
2005

Citing 2
Cited 0

An optimal algorithm for approximate nearest neighbor searching fixed dimensions

Journal of the ACM (JACM)
Automatic segmentation of the articular cartilage in knee MRI using a hierarchical multi-class classification scheme

MICCAI'05 Proceedings of the 8th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I

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Abstract

We have developed a method for segmenting tibial and femoral medial cartilage in MR knee scans by combining two k Nearest Neighbors (kNN) classifiers for the cartilage classes with a rejection threshold for the background class. We show that with this threshold, two binary classifiers are sufficient compared to three binary classifiers in the traditional one-versus-all approach. We also show that the combination of binary classifiers produces better results than a kNN classifier that is trained to partition the voxels directly into three classes. The resulting sensitivity, specificity and Dice volume overlap of our method are 84.2%, 99.9% and 0.81 respectively. Compared to state-of-the-art segmentation methods, our method outperforms a fully automatic method and is comparable to a semi-automatic method.