Differential blood count is a standard method in hematological laboratory diagnosis. In the course of developing a computer-assisted microscopy system for the generation of differential blood counts, the detection and segmentation of white and red blood cells forms an essential step and its exactness is a fundamental prerequisite for the effectiveness of the subsequent classification step. We propose a method for the exact segmentation of leukocytes and erythrocytes in a simultaneous and cooperative way. We combine pixel-wise classification with template matching to locate erythrocytes and use a level-set approach in order to get the exact cell contours of leukocyte nucleus and plasma regions as well as erythrocyte regions. An evaluation comparing the performance of the algorithm to the manual segmentation performed by several persons yielded good results.

Fiber optics are widely used in flexible endoscopes which are indispensable for many applications in diagnosis and therapy. Computeraided use of fiberscopes requires a digital sensor mounted at the proximal end. Most commercially available cameras for endoscopy provide the images by means of a regular grid of color filters what is known as the Bayer Pattern. Hence, the images suffer from false colored spatial moiré, which is further stressed by the downgrading fiber optic transmission yielding a honey comb pattern. To solve this problem we propose a new approach that extends the interpolation between known intensities of registered fibers to multi channel color applications. The inventive idea takes into account both the Gaussian intensity distribution of each fiber and the physical color distribution of the Bayer pattern. Individual color factors for interpolation of each fiber area make it possible to simultaneously remove both the comb structure from the fiber bundle as well as the Bayer pattern mosaicking from the sensor while preserving depicted structures and textures in the scene.

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The exact segmentation of nucleus and plasma of a white blood cell (leukocyte) is the basis for the creation of an automatic, image based differential white blood cell count(WBC). In this contribution we present an approach for the according segmentation of leukocytes. For a valid classification of the different cell classes, a precise segmentation is essential. Especially concerning immature cells, which can be distinguished from their mature counterparts only by small differences in some features, a segmentation of nucleus and plasma has to be as precise as possible, to extract those differences. Also the problems with adjacent erythrocyte cells and the usage of a LED illumination are considered. The presented approach can be separated into several steps. After preprocessing by a Kuwahara-filter, the cell is localized by a simple thresholding operation, afterwards a fast-marching method for the localization of a rough cell boundary is defined. To retrieve the cell area a shortest-path-algorithm is applied next. The cell boundary found by the fast-marching approach is finally enhanced by a post-processing step. The concluding segmentation of the cell nucleus is done by a threshold operation. An evaluation of the presented method was done on a representative sample set of 80 images recorded with LED illumination and a 63-fold magnification dry objective. The automatically segmented cell images are compared to a manual segmentation of the same dataset using the Dice-coefficient as well as Hausdorff-distance. The results show that our approach is able to handle the different cell classes and that it improves the segmentation quality significantly

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