Improving the classification accuracy of the classic RF method by intelligent feature selection and weighted voting of trees with application to medical image segmentation

Authors:
Mohammad Yaqub;M. Kassim Javaid;Cyrus Cooper;J. Alison Noble
Affiliations:
Institute of Biomedical Engineering, Dept. of Engineering Science, University of Oxford and Nuffield Dept. of Orthopaedics, Rheumatology & Musculoskeletal Sciences, University of Oxford;Nuffield Dept. of Orthopaedics, Rheumatology & Musculoskeletal Sciences, University of Oxford;Nuffield Dept. of Orthopaedics, Rheumatology & Musculoskeletal Sciences, University of Oxford;Institute of Biomedical Engineering, Dept. of Engineering Science, University of Oxford
Venue:
MLMI'11 Proceedings of the Second international conference on Machine learning in medical imaging
Year:
2011

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Abstract

Enhancement of the Random Forests to segment 3D objects in different 3D medical imaging modalities. More accurate voxel classification is achieved by intelligently selecting "good" features and neglecting irrelevant ones; this also leads to a faster training. Moreover, weighting each tree in the forest is proposed to provide an unbiased and more accurate probabilistic decision during the testing stage. Validation is performed on adult brain MRI and 3D fetal femoral ultrasound datasets. Comparisons between the classic Random Forests and the proposed new one show significant improvement on segmentation accuracy. We also compare our work with other techniques to show its applicability.