Analyzing bone marrow is an important task for diagnosing diseases like certain types of leukemia and anemia. There are many different cell types in bone marrow. A certain ratio of these types is characteristic for a healthy human. Each deviation from that ratio is a significant indicator for diseases. Until now determining the ratio is done manually by an expert by counting and classifying the cells with a microscope. This is cumbersome and very time consuming. So there are efforts to automatize the cell counting. The most difficult step to achieve that is the automatic segmentation of leukocytes in bone marrow smears. Because the segmentation quality of existing algorithms is not good enough a new algorithm was developed in the scope of this paper. This new algorithm is robust concerning variations as color fluctuations in the bone marrow images. The evaluation of this algorithm was done by comparing the segmentation results with the results obtained by an existing algorithm. Therefore a set of 27 bone marrow images was segmented and compared against a manual annotation. The segmentation quality obtained by the state of the art algorithm was 0.4544 and the quality achieved by the novel algorithm was 0.645 on a scale from zero to one, zero representing only invalid segmentations and one representing only perfect segmentations
For medical diagnosis, blood is an indispensable indicator for a wide variety of diseases, i.e. hemic, parasitic and sexually transmitted diseases. A robust detection and exact segmentation of white blood cells (leukocytes) in stained blood smears of the peripheral blood provides the base for a fully automated, image based preparation of the so called differential blood cell count in the context of medical laboratory diagnostics. Especially for the localization of the blood cells and in particular for the segmentation of the cells it is necessary to detect the working area of the blood smear. In this contribution we present an approach for locating the so called counting area on stained blood smears that is the region where cells are predominantly separated and do not interfere with each other. For this multiple images of a blood smear are taken and analyzed in order to select the image corresponding to this area. The analysis involves the computation of an unimodal function from image content that serves as indicator for the corresponding image. This requires a prior segmentation of the cells that is carried out by a binarization in the HSV color space. Finally, the indicator function is derived from the number of cells and the cells' surface area. Its unimodality guarantees to find a maximum value that corresponds to the counting areas image index. By this, a fast lookup of the counting area is performed enabling a fully automated analysis of blood smears for medical diagnosis. For an evaluation the algorithm's performance on a number of blood smears was compared with the ground truth information that has been defined by an adept hematologist.
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