Rapid Mode Estimation for 3D Brain MRI Tumor Segmentation

Abstract. In this work we develop a method for the efficient automated segmentation of brain tumors by developing a rapid initialization method. Brain tumor segmentation is crucial for brain tumor resection planning, and a high-quality initialization may have a significant impact on segmentation quality. The main contribution of our work is an efficient method to initialize the segmentation by casting it as nonparametric density mode estimation, and developing a Branch and Bound-based method to efficiently find the mode (maximum) of the density function. Our technique is exact, has guaranteed convergence to the global optimum, and scales logarithmically in the volume dimensions by virtue of recursively subdividing the search space through Branch-and-Bound. Our method employs the Dual Tree data structure originally developed for nonparametric density estimation, and recently used for object detection with branch-and-bound. In this work we ‘close the loop’, and use the Dual Tree da...

• Type: Journal
• Year: 2013
• Where: EMMCVPR
• Authors: Haithem Boussaid, Iasonas Kokkinos, Nikos Paragios
• Keywords: Computer Vision, EMMCVPR 2013