A data clustering based vessel segmentation method is proposed for automatic liver vasculature segmentation
in CT images. It consists of a novel similarity measure which incorporates the spatial context, vesselness information
and line-direction information in a unique way. By combining the line-direction information and spatial
information into the data clustering process, the proposed method is able to take care of the fine details of the
vessel tree and suppress the image noise and artifacts at the same time. The proposed algorithm has been evaluated
on the real clinical contrast-enhanced CT images, and achieved excellent segmentation accuracy without
any experimentally set parameters.
Robust and efficient segmentation tools are important for the quantification of 3D liver and liver tumor volumes which
can greatly help clinicians in clinical decision-making and treatment planning. A two-module image analysis procedure
which integrates two novel semi-automatic algorithms has been developed to segment 3D liver and liver tumors from
multi-detector computed tomography (MDCT) images. The first module is to segment the liver volume using a flippingfree
mesh deformation model. In each iteration, before mesh deformation, the algorithm detects and avoids possible
flippings which will cause the self-intersection of the mesh and then the undesired segmentation results. After flipping
avoidance, Laplacian mesh deformation is performed with various constraints in geometry and shape smoothness. In the
second module, the segmented liver volume is used as the ROI and liver tumors are segmented by using support vector
machines (SVMs)-based voxel classification and propagational learning. First a SVM classifier was trained to extract
tumor region from one single 2D slice in the intermediate part of a tumor by voxel classification. Then the extracted
tumor contour, after some morphological operations, was projected to its neighboring slices for automated sampling,
learning and further voxel classification in neighboring slices. This propagation procedure continued till all tumorcontaining
slices were processed. The performance of the whole procedure was tested using 20 MDCT data sets and the
results were promising: Nineteen liver volumes were successfully segmented out, with the mean relative absolute volume
difference (RAVD), volume overlap error (VOE) and average symmetric surface distance (ASSD) to reference
segmentation of 7.1%, 12.3% and 2.5 mm, respectively. For live tumors segmentation, the median RAVD, VOE and
ASSD were 7.3%, 18.4%, 1.7 mm, respectively.
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