In this paper we propose and tune a discriminative model based on Random Forest (RF) to accomplish brain tumor segmentation in multimodal MR images. The objective of tuning is meant to establish the optimal parameter values and the most significant constraints of the discriminative model. During the building of the RF classifier, the algorithm evaluates the importance of variables, the proximities between data instances and the generalized error. These three properties of RF are employed to optimize the segmentation framework. At the beginning the RF is tuned for variable importance evaluation, and after that it is used to optimize the segmentation framework. The framework was tested on unseen test images from BRATS. The results obtained are similar to the best ones presented in previous BRATS Challenges.
CITATION STYLE
Lefkovits, L., Lefkovits, S., & Szilágyi, L. (2016). Brain tumor segmentation with optimized random forest. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10154 LNCS, pp. 88–99). Springer Verlag. https://doi.org/10.1007/978-3-319-55524-9_9
Mendeley helps you to discover research relevant for your work.