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.
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Yaqub, M., Javaid, M.K., Cooper, C., Noble, J.A. (2011). Improving the Classification Accuracy of the Classic RF Method by Intelligent Feature Selection and Weighted Voting of Trees with Application to Medical Image Segmentation. In: Suzuki, K., Wang, F., Shen, D., Yan, P. (eds) Machine Learning in Medical Imaging. MLMI 2011. Lecture Notes in Computer Science, vol 7009. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24319-6_23
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DOI: https://doi.org/10.1007/978-3-642-24319-6_23
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