Method for probabilistic boosting tree-based lesion segmentation in e.g. ultrasound image data for diagnosis of liver tumors, involves minimizing error to select base classifiers to compute total classifiers in probabilistic boosting tree

摘要

The method involves applying a probabilistic boosting tree (PBT)-based lesion recognition on image points of an image data set to be evaluated using total classifiers and a base classifiers (B) determined by training. Positively recognized lesions are marked and/or output. An error during allocating the image points by the total classifiers as lesions and cost factors for a number of computations is minimized for selecting the base classifiers for computing the total classifiers in the PBT during AdaBoost training. An independent claim is also included for a processing unit i.e. computed tomography (CT) system.