Abstract
This paper extends a previously published brain tumor segmentation methods based on Random Decision Forest (RDF). An iterative approach is used in training the RDF in each iteration some patients are added to the training data using some heuristics approach instead of randomly selected training dataset. Feature extraction and selection were applied to select the most discriminative features for training our Random Decision forest on. The post-processing phase has a morphological filter to deal with misclassification errors. Our method is capable of detecting the tumor and segmenting the different tumorous tissues of the glioma achieving competitive results.
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© 2016 Springer International Publishing AG
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Ellwaa, A. et al. (2016). Brain Tumor Segmantation Using Random Forest Trained on Iteratively Selected Patients. In: Crimi, A., Menze, B., Maier, O., Reyes, M., Winzeck, S., Handels, H. (eds) Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. BrainLes 2016. Lecture Notes in Computer Science(), vol 10154. Springer, Cham. https://doi.org/10.1007/978-3-319-55524-9_13
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DOI: https://doi.org/10.1007/978-3-319-55524-9_13
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