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The diagnosis and treatment of malaria infection requires detectingthe presence of malaria parasite in the patient as well as identification of the parasite species. We present an image processing-basedapproach to detect parasites in microscope images of blood smear andan ontology-based classification of the stage of the parasite for identifying the species of infection. This approach is patterned after the diagnosis approach adopted by a pathologist for visual examination and hence is expected to deliver similar results. We formulateseveral rules based on the morphology of the basic components of a parasite namely chromatin dot(s) and cytoplasm to identify parasite stage and species. Numerical results are presented for data taken from various patients. Sensitivity of 88% and specificity of 95% is reported by evaluation of the scheme on 55 images.

C-arm based tomographic 3D imaging is applied in an increasing number of minimal invasive procedures. Due to the limited acquisition speed for a complete projection data set required for tomographic reconstruction, breathing motion is a potential source of artifacts. Intra-scan motion estimation and compensation is required. Here, a scheme for projection based local breathing motion estimation is combined with an anatomy adapted interpolation strategy and subsequent motion compensated filtered back projection. This approach is applied in animal experiments on a flat panel C-arm system delivering improved image quality in 3D liver tumor imaging.