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This thesis presents a photmetric stereo method based on the work of Schulze [35], who in turn extended the research of Schroeder et al. [33,34] In this approach, three different lightings are obtained by illuminating the object by three colored light sources (red, green and blue). A video of the subject is captured from the front, the back and the side. The single frames are then extracted from the viedo, which are used for the 3D reconstruction of the subject. The aim of this work was to improve the presented method of Schulze with real patient subjects by getting a better sphere calibration and changing some parameters in the patient processing. As the graphical interface was implemented for persons with a technical background, it has been changed to become also more convenient to use for non-technically oriented staff
Segmentation of the Cerebrospinal Fluid from MRI Images for the Treatment of Disc Herniations
(2010)
About 80 percent of people are affected at some point in their lives by lower back pain, which is one of the most common neurological diseases and reasons for long-term disability in the United States. The symptoms are primarily caused by overly heavy lifting and/or overstretching of the back, leading to a rupture and an outward bulge of an intervertebral disc, which puts pressure on and pinches the nerve fibers of the spine. The most common form is a lumbar disc herniation between the fourth and fifth lumbar vertebra and between the fifth lumbar vertebra and the sacrum. In recent years the diagnosis of lower back pain has improved, mainly due to enhanced imaging techniques and imaging quality, but the surgical therapy remains hazardous. Reasons for this include low visibility when accessing the lumbar area and the high risk of causing permanent damage when touching the nerve fibers. A new approach for increasing patient safety is the segmentation and visualization of the cerebrospinal fluid in the lower lumbar region of the vertebral column. For this purpose a new fully-automatic and a semi-automatic approach were developed for separating the cerebrospinal fluid from its surroundings on T2-weighted MRI scans of the lumbar vertebra. While the fully-automatic algorithm is realized by a model-based searching method and a volume-based segmentation, the semi-automatic algorithm requires a seed point and performs the segmentation on individual axial planes through a combination of a region-based segmentation algorithm and a thresholding filter. Both algorithms have been applied to four T2-weighted MRI datasets and are compared with a gold-standard segmentation. The segmentation overlap with the gold-standard was 78.7 percent for the fully-automatic algorithm and 93.1 percent for the semi-automatic algorithm. In the pathological region the fully-automatic algorithm obtained a similarity of 56.6 percent, compared to 87.8 percent for the semi-automatic algorithm.
Quantitative assessment of Positron Emission Tomography (PET) imaging can be used for diagnosis and staging of tumors and monitoring of response in cancer treatment. In clinical practice, PET analysis is based on normalized indices such as those based on the Standardized Uptake Value (SUV). Although largely evaluated, these indices are considered quite unstable mainly because of the simplicity of their experimental protocol. Development and validation of more sophisticated methods for the purposes of clinical research require a common open platform that can be used both for prototyping and sharing of the analysis methods, and for their evaluation by clinical users. This work was motivated by the lack of such platform for longitudinal quantitative PET analysis. By following a prototype driven software development approach, an open source tool for quantitative analysis of tumor changes based on multi-study PET image data has been implemented. As a platform for this work, 3D Slicer 4, a free open source software application for medical image computing has been chosen. For the analysis and quantification of PET data, the implemented software tool guides the user through a series of workflow steps. In addition to the implementation of a guided workflow, the software was made extensible by integration of interfaces for the enhancement of segmentation and PET quantification algorithms. By offering extensibility, the PET analysis software tool was transformed into a platform suitable for prototyping and development of PET-specific segmentation and quantification methods. The accuracy, efficiency and usability of the platform were evaluated in reproducibility and usability studies. The results achieved in these studies demonstrate that the implemented longitudinal PET analysis software tool fulfills all requirements for the basic quantification of tumors in PET imaging and at the same time provides an efficient and easy to use workflow. Furthermore, it can function as a platform for prototyping of PET-specific segmentation and quantification methods, which in the future can be incorporated in the workflow.
The e-commerce turnover has a constant growth rate of about 10%. An additional increase
in complexity and traffic spikes clarify the need for a scalable software architecture to prevent
a potential technical debt, higher financial cost, longer maintenance, or a reduced reliability.
Due to the fact, that existing approaches like the Palladio Approach require a high modelling
overhead and the importance of dropping this overhead was identified this master thesis is
focused on the modelling and simulation of e-commerce web application architectures using
a high-level approach to provide a faster, but possibly more inaccurate prediction of the
scalability.
This is done by the usage of the Design Science Research Process as a frame, a scientific
literature review for use of the existing knowledge base and the Conical Methodology for the
artefact creation. The artefact is a graphical model which is evaluated using a simulation
developed with Python and its framework SimPy. For model creation and evaluation a total
of twelve papers investigating the scalability of e-commerce web application architectures is
split into a test and train group. The training group and parts of the scientific research are
used to identify the components load balancer, application server, web tier, ERP system,
legacy system and database as well as some general characteristics that need to be considered.
The components with the most modelling variables are the application server and web
tier with a total of thirteen, while the ERP and legacy system only required five.
The model is evaluated using a total of three papers from the test group, where an average
throughput error of 5.78% and a response time error of 46.55% or 26.46% was identified. An
additional evaluation based on two non-e-commerce architectures shows the usability of the
model for other types of architectures. Even though the average error gives the impression,
that the model is not providing a good estimation, the graphical results show, that the model
and its simulation can be used to provide a faster scalability prediction. The model is least
accurate for the prediction of the situation, where the response time increases exponentially,
as this is the point, where variables, only accountable for some percentage and thus ignored
for the model, have the highest influence.
Future research can be found in the extension of the model by either adding or investigating
additional components, adding features ignored within this work or applying it to other
types of web application architectures. Additionally, both the low-level and the high-level
approaches can be brought together to combine the advantages from both approaches.
eHMIS is a Ugandan Hospital Information System (HIS), which targets the Sub-Saharan market. In its first version all forms were programmed statically and adaptations were done by code modifications. In 2014 the development of a second version of eHMIS based on Java started.
This work aims at introducing dynamic forms to this new version. While forms that are significantly important to the workflow of the application will remain static, others are replaced by forms that are dynamically designed by the user. By that, the application will become more flexible and local and situational tailoring will be possible without inducing extra costs.
In this thesis the design, implementation and testing of dynamic forms in eHMIS is discussed. The architecture is based on the questionnaire resource of FHIR®. The module enables the user to create questions and group them into sections and questionnaires. For each question the type of answer expected and other constraints can be defined. A user interface covering all functions was designed, so that no programming skills are required. In a first step dynamic forms were integrated in the application's workflow for recording symptoms, though other fields of application are possible. For testing, a usability experiment was conducted in Tororo Hospital in Eastern Uganda, using the thinking aloud method. Results were analysed and evaluated to detect usability problems and gain a general impression of user satisfaction.
In this thesis a software system is proposed that provides transparent access to dynamically processed data using a synthetic filesystem for the data transfer as well as interaction with the processing pipeline. Within this context the architecture for such a software solution has been designed and implemented. Using this implementation various profiling measurements have been acquired in order to evaluate the applicability in different data processing scenarios. Usability aspects, considering the interaction with the processing pipeline, have been examined as well. The implemented software is able to generate the processing result on-the-fly without modification of the original input data. Access to the output data is provided by means of a common filesystem interface without the need of implementing yet another communication protocol. Within the processing pipeline the data can be accessed and modified independently from the actual input and output encoding. Currently the data can be modified using a C/C++, GLSL or Java front end. Profiling data has shown that the overhead induced by the filesystem is negligible for most usage patterns and is only critical for realtime processing with a high data throughput e. g. video processing at or above 30 frames per second where typically no file operations are involved.
The Greifswald University Hospital in Germany conducts a research project called "Greifswald Approach to Individualized Medicine (GANI_MED)", which aims at improving patient care through personalized medicine. As a result of this project, there are multiple regional patient cohorts set up for different common diseases. The collected data of these cohorts will act as a resource for epidemiological research. Researchers are going to get the possibility to use this data for their study, by utilizing a variety of different descriptive metadata attributes. The actual medical datasets of the patients are integrated from multiple clinical information systems and medical devices. Yet, at this point in the process of defining a research query, researchers do not have proper tools to query for existing patient data. There are no tools available which offer a metadata catalogue that is linked to observational data, which would allow convenient research. Instead, researchers have to issue an application for selected variables that fit the conditions of their study, and wait for the results. That leaves the researchers not knowing in advance, whether there are enough (or any) patients fitting the specified inclusion and exclusion criteria. The "Informatics for Integrating Biology and the Bedside (i2b2)" framework has been assessed and implemented as a prototypical evaluation instance for solving this issue. i2b2 will be set up at the Institute for Community Medicine (ICM) at Greifswald, in order to act as a preliminary query tool for researchers. As a result, the development of a research data import routine and customizations of the i2b2 webclient were successfully performed. An important part of the solution is, that the metadata import can adapt to changes in the metadata. New metadata items can be added without changing the import program. The results of this work are discussed and a further outlook is described in this thesis.
Implementation of an interactive pattern mining framework on electronic health record datasets
(2019)
Large collections of electronic patient records contain a broad range of clinical information highly relevant for data analysis. However, they are maintained primarily for patient administration, and automated methods are required to extract valuable knowledge for predictive, preventive, personalized and participatory medicine. Sequential pattern mining is a fundamental task in data mining which can be used to find statistically relevant, non-trivial temporal dependencies of events such as disease comorbidities. This works objective is to use this mining technique to identify disease associations based on ICD-9-CM codes data of the entire Taiwanese population obtained from Taiwan’s National Health Insurance Research Database.
This thesis reports the development and implementation of the Disease Pattern Miner – a pattern mining framework in a medical domain. The framework was designed as a Web application which can be used to run several state-of-the-art sequence mining algorithms on electronic health records, collect and filter the results to reduce the number of patterns to a meaningful size, and visualize the disease associations as an interactive model in a specific population group. This may be crucial to discover new disease associations and offer novel insights to explain disease pathogenesis. A structured evaluation of the data and models are required before medical data-scientist may use this application as a tool for further research to get a better understanding of disease comorbidities.
Ambulant studies are dependent on the behavior and compliance of subjects in their home environment. Especially during interventions on the musculoskeletal system, monitoring physical activity is essential, even for research on nutritional, metabolic, or neuromuscular issues. To support an ambulant study at the German Aerospace Center (DLR), a pattern recognition system for human activity was developed. Everyday activi-ties of static (standing, sitting, lying) and dynamic nature (walking, ascending stairs, descending stairs, jogging) were under consideration. Two tri-axial accelerometers were attached to the hip and parallel to the tibia. Pattern characterizing features from the time domain (mean, standard deviation, absolute maximum) and the frequency domain (main frequencies, spectral entropy, autoregressive coefficients, signal magni-tude area) were extracted. Artificial neural networks (ANN) with a feedforward topology were trained with backpropagation as supervised learning algorithm. An evaluation of the resulting classifier was conducted with 14 subjects completing an activity protocol and a free chosen course of activities. An individual ANN was trained for each subject. Accuracies of 87,99 % and 71,23 % were approached in classifying the activity protocol and the free run, respectively. Reliabilities of 96,49 % and 76,77 % were measured. These performance parameters represent a working ambulant physical activity monitor-ing system.
There are many drug interactions and to know every single interaction is impossible. In Uganda, a country located in East Africa, patients often do not get a patient information leflaet when a physician prescribes drugs because they only get the drugs without packaging and information inside. Even in developed countries many poeple die because of drug interactions.
This work aims at developing a clinical decision support system for different kinds of drug interactions: 1) drug-drug interaction,
2) drug-food interactions,
3) drug-condition
interactions and
4) drug-disease interactions.
This system must be integrated into an
existing hospital information system called electronic Health Management Information System (eHMIS).
In the first part of this thesis different kinds of clinical decision support systems are described to find out which one is the best for eHMIS. The two different types are knowledge-based and non knowledge-based systems. The second part of this thesis, the data base of eHMIS is extended to have a full
knowledge base for the new module which contains drug-drug interactions, drug-food interactions, drug-disease interactions as well as drug-condition interactions. Therefore new tables were created and filled with data of several data bases with drug interactions. The last part is about designing the clinical decision support system for drug interactions
with the knowledge base of eHMIS, including the implementation considering the integration into the existing system. To know how health professionals in Uganda work
with an electronical health system as well as their other work ows was important. The system now runs in a hospital in Kampala, the capital of Uganda and in a health center level three in Mifumi, a village located in the east of the country.