Coastal defense mechanisms are an integral part in the safety of infrastructure and communities residing on coastlines around the globe. In long temporal and spatial scales, traditional “hard structures” for coastal defense can become infeasible. Incorporation of soft engineering methods for coastal defense can then be a viable solution. Vegetation belts along the coasts can be a prominent soft engineering application. Many previous studies have been done to identify the protection offered by coastal vegetation. This thesis aims to identify mangrove vegetation interactions under hazardous wave conditions. Moreover, the study includes a comprehensive parameter space by accommodating the variation in wave climate, vegetation and bathymetry observed in the field. The hydrodynamic-mangrove interactions are analyzed from the perspective of coastal hazard mitigation by vegetation. The study focuses on wave attenuation, setup variation and runup reduction by mangroves. The investigations are carried out using a numerical modeling scheme, XBeach-Surfbeat. Mangrove vegetation can substantially mitigate the effects of coastal hazards by waves (wave energy, wave induced flooding) faced in the hinterland. However, the level of mitigation depends on several factors. The most important factors are, vegetation density, mangrove forest width, wave height and water level. Denser mangrove vegetation and wider forests increases the wave attenuation while deeper water depths in mangrove forests reduces the attenuation capacity. The improved understanding of the hydrodynamic-vegetation interactions gained in this study can be used as a foundation for a Bayesian network. A better understanding of the effect of the different parameters on flood mitigation can then be attained.

Introduction Near-infrared fluorescence (NIRF) imaging is a relatively novel development in colorectal liver metastases (CRLM) surgery which has proven to be an added value for the detection of new lesions and surgical guidance. However, new possibilities of NIRF imaging in CRLM surgery are yet to be explored. The fluorescent rim of indocyanine green (ICG) surrounding CRLM can potentially be used to predict the resection margin. By predicting the resection margin, irradical resections can be observed during surgery. Additional tissue can be removed intraoperatively, probably resulting in improved outcomes. This research describes a method to analyze the data retrieved at the Department of Pathology. The goal of this thesis is to provide a verdict on whether ICG could be used as a quantitative indicator for distance to the tumor and therefore, help the surgeon to evaluate the resection margin on-site. Materials & Methods Data was collected by imaging bread loaves from resected CRLM. Methods to generate data from these images, to evaluate the collected data, and to evaluate the interobserver error were described. The data were evaluated with the use of general and histogram parameters. Moreover, the data was separated four times into different groups. These groups aimed to investigate the general distribution of ICG around CRLM and the effect of tumor and patient-specific characteristics. Subsequently, the results of the data analysis were used to make a rough estimation of the expected measured fluorescence intensities during surgery. Results The interobserver error was 0.9 or higher (Sørensen-Dice coefficient) for 8 out of the 10 delineation pairs including three pairs with a coefficient of 0.95 or higher. However, two pairs of delineations showed coefficients of 0.566 and 0.797. 33 bread loaves were included in this research for data analysis of the general and histogram parameters. Various distributions of ICG around the tumors were found for the groups varying in tumor and patient characteristics. Moreover, differences between groups in general parameters were observed. The rough estimation of the expected measured fluorescence intensity showed a peak intensity around 5 millimeters from the tumor. Discussion The delineation of CRLM proved to be a reproducible operation in most of the delineations (80%). However, there is a need for ground truth delineations to examine the accuracy of delineations. Besides, the effects of chemotherapy, the location of the tumor, and the size of the tumor on the peritumoral fluorescent rim, as well as the flaws of this research were discussed. Future recommendations Additional research must be performed to be able to develop a method to evaluate resection margins intraoperatively. Future research should focus on the formulation of an implementation plan and the development of the database, the MeVisLab network, the resection margin prediction method, and a protocol that enables the opportunity to create ground truth delineations. Conclusion This study showed the potential of the use of the peritumoral fluorescent rim to develop a method that can predict resection margins of resected CRLM intraoperatively. However, additional research must be performed to accurately implement the method and to increase its accuracy.

While millions of people world wide suffer from arterial diseases, such as peripheral arterial disease, there are a limited number of methods that can be used to diagnose and track these diseases which are also easy, quick and non-invasive. This work focuses on what is needed to improve diagnosing and tracking of peripheral arterial disease (PAD) using visualisation techniques. Visualising the blood flow pulsation in the skin can be useful in cases of arterial diseases, as the diseases can influence the blood flow by obstructions and arterial stiffness. The main objective in the visualisation is to show the acceleration of the blood flow, as this is linked to the arterial stiffness. The proposed algorithm for visualising the acceleration of the blood flow is comprised of multiple steps, including techniques such as motion reduction, Eulerian video magnification, remote photoplethysmography signal extraction and using the second derivative. The input of this algorithm are videos of the skin of patients, this makes this method easy and non-invasive. Photoplethysmography (PPG) signals are present in videos of skin, but can not be seen with the naked eye. Using Eulerian video magnification the PPG signals are amplified for better processing and visibility. By combining groups of pixels into small patches, a decrease in processing time is achieved and it adds a filtering effect. The size of the patches controls the resolution of the visualisation. Movement from the camera or patient is detrimental when extracting the PPG signal from the video. To counteract the motion in the videos a motion reduction step, using optical flow, is applied. Using the Plane-orthogonal-to-skin (POS) algorithm, the signal extracted from videos is converted to a PPG signal. Calculating the second derivative of the PPG signal gives the acceleration of the signal. By splitting the acceleration signal into positive and negative numbers, the acceleration and deceleration of the blood flow is visualised. Synthetic videos simulating the skin were generated in various levels of accuracy to aid the development of the algorithm and to conduct experiments. The levels range from a simple pulsating square to a moving PPG signal over a blood vessel like structure. In addition, real videos of patients were used. The experiments show the feasibility of visualising the acceleration of the blood flow pulsation in the skin, but also highlights areas of improvements and future research. More fine-tuning of the algorithm is needed, in addition to acquiring more videos of patients with PAD before and after surgery in a controlled environment. A working proof of concept of the algorithm is shown. It has the potential of being a novel method of diagnosing and tracking arterial diseases.