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In this thesis, different approaches to calibrate an optical microscope will be presented. State-of-the-art algorithms are implemented and improvements are done to get more stable results and make the calibration process more flexible. Algorithms are developed to calibrate some of the camera parameters in advance in order to achieve better end results for the calibration process. In addition, techniques will be discussed on how to reduce errors produced by noise on a given input data set so that correct parameters for a specific camera model can be calibrated. These algorithms are evaluated and compared on real and syntetic data sets.
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.