Date Added: Jul 2009
In this paper, the authors present a novel probabilistic framework for automatic follicle quantification in 3D ultrasound data. The proposed framework robustly estimates size and location of each individual follicle by fusing the information from both global and local context. Follicle candidates at detected locations are then segmented by a novel database guided segmentation method. To efficiently search hypothesis in a high dimensional space for multiple object detection, a clustered marginal space learning approach is introduced.