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Institute
The cytological examination of bone marrow serves as clarification of variations in blood smears. It is also used for the clarification of anemia, as exclusion of bone marrow affection at lymphoma and at suspicion of leukemia. The morphological evaluation of hematopoietic cells is the basis for the creation of the diagnosis and for decision support for further diagnostics. Even for experienced hematologists the manual classification of hematopoietic cells is time-consuming, error-prone and subjective. For this reason new methods in the field of image processing and pattern recognition for the automatic classification including preprocessing steps are developed for a computer-assisted microscopy system. These methods are evaluated by means of a huge reference database. The proposed image analysis procedures comprise methods for the automated detection of smears, for the determination of relevant regions, for the localization and segmentation of single hematopoietic cells as well as for the feature extraction and classification task. These methods provide the basis for the first system for the automated, morphological analysis of bone marrow aspirates for leukemia diagnosis and are therefore a major contribution for a better and more efficient patient care in the future.
Data Mining im Fußball
(2014)
The term Data Mining is used to describe applications that can be applied to extract useful information from large datasets. Since the 2011/2012 season of the german soccer league, extensive data from the first and second Bundesliga have been recorded and stored. Up to 2000 events are recorded for each game.
The question arises, whether it is possible to use Data Mining to extract patterns from this extensive data which could be useful to soccer clubs.
In this thesis, Data Mining is applied to the data of the first Bundesliga to measure the value of individual soccer players for their club. For this purpose, the state of the art and the available data are described. Furthermore, classification, regression analysis and clustering are applied to the available data. This thesis focuses on qualitative characteristics of soccer players like the nomination for the national squad or the marks players get for their playing performance. Additionally this thesis considers the playing style of the available players and examines if it is possible to make predictions for upcoming seasons. The value of individual players is determined by using regression analysis and a combination of cluster analysis and regression analysis.
Even though not all applications can achieve sufficient results, this thesis shows that Data Mining has the potential to be applied to soccer data. The value of a player can be measured with the help of the two approaches, allowing simple visualization of the importance of a player for his club.