Tissue Classification to Support Local Active Delineation of Brain Tumors

In this paper, we demonstrate how a semi-automatic algorithm we proposed in previous work may be integrated into a protocol which becomes fully automatic for the detection of brain metastases. Such a protocol combines 11C-labeled Methionine PET acquisition with our previous segmentation approach. We show that our algorithm responds especially well to this modality thereby upgrading its status from semi-automatic to fully automatic for the presented application. In this approach, the active contour method is based on the minimization of an energy functional which integrates the information provided by a machine learning algorithm. The rationale behind such a coupling is to introduce in the segmentation the physician knowledge through a component capable of influencing the final outcome toward what would be the segmentation performed by a human operator. In particular, we compare the performance of three different classifiers: Naive Bayes classification, K-Nearest Neighbor classification, and Discriminant Analysis. A database comprising seventeen patients with brain metastases is considered to assess the performance of the proposed method in the clinical environment.