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We present a new method and validation study for the nearly automatic segmentation of liver tumors. The method is part of a nearly automatic system for simultaneous segmentation of liver contours, vessels, and tumors from abdominal CTA scans. It repeatedly applies multiresolution, multi-class smoothed Bayesian classification followed by morphological(More)
We present a new method for the simultaneous, nearly automatic segmentation of liver contours, vessels, and metastatic lesions from abdominal CTA scans. The method repeatedly applies multi-resolution, multi-class smoothed Bayesian classification followed by morphological adjustment and active contours refinement. It uses multi-class and voxel neighborhood(More)
We present a new algorithm for nearly automatic liver segmentation and volume estimation from abdominal Computed Tomography Angiography (CTA) images and its validation. Our hybrid algorithm uses a multiresolution iterative scheme. It starts from a single user-defined pixel seed inside the liver, and repeatedly applies smoothed Bayesian classification to(More)
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