Deformable Image Registration (DIR) is a process in which the point-to-point correspondence between two or more medical images is estimated. This could allow spatial data to be transferred between these images, easing the work of practitioners in the field of radiation oncology. Many DIR approaches already exist. These are not yet applicable in all clinical settings, however, as DIR is a complex problem, partly because of the difficulty of evaluating image registration results. This is why often multiple objectives are considered in this evaluation. In this thesis, we combine two separate multi-objective DIR approaches into hybrids while evaluating their performances, with the aim to improve upon the individual approaches, thus reducing the gap toward clinical practice. In these hybrids, we also use an approach called Automated Landmark Detection which automatically detects corresponding landmarks between the source and target images.

The first DIR approach, the Digital Phantom, creates a biomechanical model of the patient and uses an Evolutionary Algorithm (EA) to optimize parameters of a Finite Element Method (FEM)-based simulation. The second DIR approach, MOREA, creates a dual-dynamic biomechanical transformation model of the patient, using an EA to optimize its parameters.

In the first hybrid approach, called the Landmark Guided Digital Phantom (LGDP), the Automated Landmark Detection approach is used to automatically find corresponding landmarks, which are then used as a guidance objective in the Digital Phantom. The second hybrid is called DP-MOREA, where the output of the Digital Phantom is used to initialize deformations in the MOREA approach. The final hybrid is called LGDP-MOREA. This hybrid combines all three components, using the generated landmarks to guide the Digital Phantom and initializing MOREA with the result. We analyze the effect of these hybrids on the quality of the final registration, both quantitatively and qualitatively. Additionally, we compare these hybrids with the components they are composed of.

We test the performance of the approaches and hybrids on pelvic CT scans of three cervical cancer patients with large deformations. We observe that the individual Digital Phantom component outperforms the LGDP hybrid both quantitatively and qualitatively on these three patients. In addition, we observe that initializing MOREA with the output of the Digital Phantom and LGDP improves on how fast the optimization converges, that it improved the end results quantitatively and that it also has an effect on the resulting Deformation Vector Fields (DVFs). We conclude that in future research, these effects on the DVFs and their clinical relevance should be studied in more detail.

Music indexing, the practice of identifying songs contained in an audio sample, is an approach that is widely used. As an underlying technique, "audio fingerprinting" can be used. In this technique, an audio sample is converted to a fingerprint; a smaller representation of the audio. This fingerprint is compared to a database of fingerprinted songs in order to retrieve the original song. In this research, we aim to answer the question of how music in movies can be identified using an audio fingerprinting platform called Audioneex. Two configurable parameters in the framework have been varied, of which the configurations have been evaluated in terms of a benchmark that has been established on synthesized data. The best performing configuration found in the limits of the parameters has been compared to the base configuration of Audioneex. Based on the results, the selected configuration improved the performance of the Audioneex framework by 1 match for one matching algorithm and by 3 matches for the other available matching algorithm.