Daniel Stromer
Non-invasive imaging methods are capable of revealing content that is not visible with the naked eye. In this thesis, we present solutions for three common areas of application: digitization of cultural heritage, medicine, and quality assessment.
The success story of those approaches is furthermore driven by applying image processing methods allowing a human to investigate the data by the naked eye. In our work, we refer to the combination of appropriate imaging techniques combined with data processing algorithms.
In case of cultural heritage digitization, we propose complete pipelines – from scanning to the final two-dimensional writing outputs. Such entire processing blocks are presented for historical books written with metallic inks used since the Roman Empire. The method is capable of processing X-ray computed tomography scans of closed books such that the pages are finally readable by the naked eye. The second example is a bamboo scroll as it was used in ancient China. A virtual cleaning followed by a virtual unwrapping step is used to reveal the hidden content of heavily soiled documents. Also this method is based on X-rays to generate the volumetric data.
Optical coherence tomography is a modality widely used by ophthalmologist to investigate retinal layers. Highly accurate segmentation as well as a common basis for researchers to evaluate methods is a major issue in this field. We developed a software framework in a collaboration between four institutions including clinical partners to tackle this problem. We integrated an automatic approach for initial segmentation of three relevant retinal layers. Inaccuracies can be eliminated by using the novel manual refinement algorithm producing highly accurate results with minimal amount of labor. In addition, visualization techniques can be used to illustrate the results immediately.
For quality assessment of photovoltaic cells, electroluminesence imaging can be utilized to reveal defects in multicrystalline solar cells. To automatically detect cracks in these cells, algorithmic approaches are used to segment such structures. We propose a segmentation technique with a run-time of less than a second for a single cell showing accurate results. As this field is mainly industry driven, we addressed the lack of open-source algorithms by making ours publicly available such that anyone can compare and evaluate novel techniques.
For all proposed methods, we show extensive quantitative and qualitative evaluations. We discuss challenges as well as improvements achieved with the novel techniques. We are confident that the presented work is a great contribution to the field of non-invasive imaging and furthermore highlights the need for harmonizing the used acquisition techniques with subsequent data processing.