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A fully automatic iterative training approach to generate discriminative shape models for usage in the Generalized Hough Transform (GHT) is presented. The method aims at capturing the shape variability of the target object contained in the training data as well as identifying confusable structures (anti-shapes) and to integrate this information into one model. To distinguish shape and anti-shape points and to determine their importance, an individual positive or negative weight is estimated for each model point with means of a discriminative training technique. The model is built from edge points surrounding the target point and the most confusable structure as identified by the GHT. Through an iterative approach, the performance of the model is gradually improved by extending the training dataset with images, where the current model failed to localize the target point. The proposed method is successfully tested on a set of 670 long-leg radiographs, where it achieves a localization rate of 74-97%.

The Discriminative Generalized Hough Transform (DGHT) is a method for object localization, which combines the standard GHT with a discriminative training technique. Thereby the aim of the discriminativetraining is to equip the models used in the GHT with individual model point weights such that the localization error in the GHT becomesminimal. In this paper we want to introduce an extension of the DGHTusing a multi-level approach to reduce processing time and to improve localization accuracy. The approach searches for the target object on multiple resolution levels and combines this information for faster and better results. The advantage of the approach is demonstrated on low-resolution, whole-body MR images, which are intended for PET attenuation correction.

The use of a priori knowledge in aligning targeted sequencing data is investigated using computational experiments. With conventional aligners such as Bowtie, BWA or MAQ, alignment is performed against the whole genome. Using an alignment method in which the genomic position information from the target capture is incorporated, alignment can be done to just the target region. Investigating the effect of realistic target size, read length, read redundancy, the amount of off-target reads and sequencing error rate, improvements of up to a factor of 8 +/- 0.3 in alignment speed are found using an implementation of the Needleman-Wunsch algorithm which makes use of direct stringcomparison. This results in a total alignment time in target sequencing of around 1 min.