VCBM 08: Eurographics Workshop on Visual Computing for Biomedicine

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https://diglib.eg.org/handle/10.2312/466

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Browsing VCBM 08: Eurographics Workshop on Visual Computing for Biomedicine by Subject "Categories and Subject Descriptors (according to ACM CCS): I.4.6 [Image Processing and Computer Vision]: Segmentation"

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    Automatic Hepatocyte Quantification from Histological Images: Comparing Pre-smoothing filters
    (The Eurographics Association, 2008) Ivanovska, Tetyana; Schenk, Andrea; Dahmen, Uta; Hahn, Horst K.; Linsen, Lars; Charl Botha and Gordon Kindlmann and Wiro Niessen and Bernhard Preim
    Quantity of hepatocytes in the liver can reveal a lot of information for medical researchers. In our project, it is needed for evaluation of the liver regeneration rate. In this paper, we present a processing pipeline for automatic counting of hepatocytes from images of histological sections. In particular, we propose to introduce a preprocessing step in form of image smoothing. We apply five different smoothing techniques, namely Gaussian smoothing, nonlinear Gaussian smoothing, median filtering, anisotropic diffusion, and minimum description length segmentation, and compare them to each other. The processing pipeline is completed by subsequent automatic thresholding using Otsu s method and hepatocyte detection using Hough transform. We compare the quantification results in terms of quality (sensitivity and specificity rates) against the manually specified ground truth. We discuss the results and limitations of the individual processing steps as well as of the overall automatic quantification approach.
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    Fully Automatic Skull-Stripping in 3D Time-of-Flight MRA Image Sequences
    (The Eurographics Association, 2008) Forkert, Nils Daniel; Säring, Dennis; Fiehler, Jens; Illies, Till; Färber, Matthias; Möller, Dietmar; Handels, Heinz; Charl Botha and Gordon Kindlmann and Wiro Niessen and Bernhard Preim
    In this paper we present a robust skull-stripping method for the isolation of cerebral tissue in 3D Time-of-Flight (TOF) magnetic resonance angiographic images of the brain. 3D TOF images are often acquired in case of cerebral vascular diseases, because of their good blood-to-background-contrast. Skull-stripping is an essential preprocessing step towards a better segmentation as well as direct visualization of the vascular system. Our approach consists of three main steps. After preprocessing in order to reduce signal inhomogeneities and noise the first main step is the segmentation of the surrounding skull using a region growing approach. The second step is the automatic extraction of distinctive points at the border of the brain, based on the segmentation of the skull, which are then used as supporting points for a graph based contour extraction. The third step is a slicewise correction based on a non-linear registration in order to improve sub-optimal segmentation results. The method proposed was validated using 18 manually stripped datasets. The calculated similarity measures show that the proposed method leads to good segmentation results with only a few segmentation errors. At the same time the mean rate of vessel voxels included by the brain segmentation is 99.18%. In summary the procedure suggested allows a fast and fully automatic segmentation of the brain and is especially helpful as a preprocessing step towards an automatic segmentation of the vessel system or direct volume rendering.
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    Reconstruction of Blood Vessels from Neck CT Datasets using Stable 3D Mass-Spring Models
    (The Eurographics Association, 2008) Dornheim, Jana; Lehmann, Dirk J.; Dornheim, Lars; Preim, Bernhard; Strauß, Gero; Charl Botha and Gordon Kindlmann and Wiro Niessen and Bernhard Preim
    Preoperative neck dissection planning benefits from a smooth, organic visualization of the main blood vessels of the neck, in particular the carotid artery and jugular vein. While most reconstruction techniques for vasculature are designed for segmenting the complete vessel tree, our goal is to isolate these specific blood vessels of the neck from the CT dataset, and to exclude irrelevant vasculature from the visualization. Pure threshold- and iso value-based reconstruction techniques do not allow such a selective segmentation and often lead to undersegmentation at the lower parts of the blood vessels, due to inhomogeneous contrast agent diffusion. In order to avoid staircase artifacts in the visualizations of the reconstructed vascular structures, a subvoxel accuracy of the reconstruction technique is also required. We present a model-based reconstruction technique to isolate blood vessels from neck CT datasets using Stable 3D Mass-Spring Models. The results can be visualized directly without staircase artifacts. The interaction needed for the reconstruction is reduced substantially to only a few clicks along the blood vessels. The presented method was evaluated with 30 blood vessels from 14 CT datasets of the neck and could be shown to be accurate, while leading to smooth visualizations of the neck blood vessels.