SVM-based failure detection of GHT localizations

This paper addresses the localization of anatomical structures in medical images by a Generalized Hough Transform (GHT). As localization is often a pre-requisite for subsequent model-based segmentation, it is important to assess whether or not the GHT was able to locate the desired object. The GHT by its construction does not make this distinction. We present an approach to detect incorrect GHT localizations by deriving collective features of contributing GHT model points and by training a Support Vector Machine (SVM) classifier. On a training set of 204 cases, we demonstrate that for the detection of incorrect localizations classification errors of down to 3% are achievable. This is three times less than the observed intrinsic GHT localization error.