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Organ segmentation in magnetic resonance (MR) volume data is of increasing interest in epidemiological studies and clinical practice. Especially in large-scale population-based studies, organ volumetry is highly relevant requiring exact organ segmentation. Since manual segmentation is time consuming and prone to reader variability, large-scale studies need automatic methods to perform organ segmentation. In this paper, we present an automated framework for renal tissue segmentation that computes renal parenchyma, cortex, and medulla volumetry in native MR volume data without any user interaction. We introduce a novel strategy of subject-specific probability map computation for renal tissue types, which takes inter- and intra-MR-intensity variability into account. Several kinds of tissue-related 2-D and 3-D prior-shape knowledge are incorporated in modularized framework parts to segment renal parenchyma in a final level set segmentation strategy. Subject-specific probabilities for medulla and cortex tissue are applied in a fuzzy clustering technique to delineate cortex and medulla tissue inside segmented parenchyma regions. The novel subject-specific computation approach provides clearly improved tissue probability map quality than existing methods. Comparing to existing methods, the framework provides improved results for parenchyma segmentation. Furthermore, cortex and medulla segmentation qualities are very promising but cannot be compared to existing methods since state-of-the art methods for automated cortex and medulla segmentation in native MR volume data are still missing.

Exact analysis of glottal vibration patterns is indispensable for the assessment of laryngeal pathologies. Increasing demand of voice related examination and large amount of data provided by high-speed laryngoscopy and stroboscopy call for automatic assistance in research and patient care. Automatic glottis segmentation is necessary to assist glottal vibration pattern analysis, but unfortunately proves to be very challenging. Previous glottis segmentation approaches hardly consider characteristic glottis features as well as inhomogeneity of glottal regions and show serious drawbacks in their application for diagnostic purposes. We developed a fully automated glottis segmentation framework that extracts a set of glottal regions in endoscopic videos by using a flexible thresholding technique combined with a refining level set method that incorporates prior glottis shape knowledge. A novel descriptor for glottal regions is presented to remove potential nonglottal fake regions that show glottis-like shape properties. Knowledge of local color distributions is incorporated into Bayesian probability image generation. Glottal regions are then tracked frame-by-frame in probability images with a region-based level set segmentation strategy. Principal component analysis of pixel coordinates is applied to determine glottal orientation in each frame and to remove nonglottal regions if erroneous regions are included. The framework shows very promising results concerning segmentation accuracy and processing times and is applicable for both stroboscopic and high-speed videos.

Fully automatic 3-D segmentation techniques for clinical applications or epidemiological studies have proven to be a very challenging task in the domain of medical image analysis. 3-D organ segmentation on magnetic resonance (MR) datasets requires a well-designed segmentation strategy due to imaging artifacts, partial volume effects, and similar tissue properties of adjacent tissues. We developed a 3-D segmentation framework for fully automatic kidney parenchyma volumetry that uses Bayesian concepts for probability map generation. The probability map quality is improved in a multistep refinement approach. An extended prior shape level set segmentation method is then applied on the refined probability maps. The segmentation quality is improved by incorporating an exterior cortex edge alignment technique using cortex probability maps. In contrast to previous approaches, we combine several relevant kidney parenchyma features in a sequence of segmentation techniques for successful parenchyma delineation on native MR datasets. Furthermore, the proposed method is able to recognize and exclude parenchymal cysts from the parenchymal volume. We analyzed four different quality measures showing better results for right parenchymal tissue than for left parenchymal tissue due to an incorporated liver part removal in the segmentation framework. The results show that the outer cortex edge alignment approach successfully improves the quality measures.