Abstract
BackgroundAutomated image segmentation has benefits for reducing clinicians’ workload, quicker diagnosis, and a standardization of the diagnosis.MethodsThis study proposes an automatic liver segmentation approach based on appearance and context information. The relationship between neighboring pixels in blocks is utilized to estimate appearance information, which is used for training the first classifier and obtaining the probability distribution map. The map is used for extracting context information, along with appearance features, to train the next classifier. The prior probability distribution map is achieved after iterations and refined through an improved random walk for liver segmentation without user interaction.ResultsThe proposed approach is evaluated using CT images with eight contemporary approaches, and it achieves the highest VOE, RVD, ASD, RMSD and MSD. It also achieves a high average score of 76 using the MICCAI-2007 Grand Challenge scoring system.ConclusionsExperimental results show that the proposed method is superior to eight other state of the art methods.
Highlights
Automated image segmentation has benefits for reducing clinicians’ workload, quicker diagnosis, and a standardization of the diagnosis
Experimental results the proposed method is evaluated on the MICCAI 2007 grand challenge data [17], which contains 20 3D abdominal CT scans with ground truth
Two scans randomly select in MICCAI 2007 grand challenge database serve as the test data
Summary
Automated image segmentation has benefits for reducing clinicians’ workload, quicker diagnosis, and a standardization of the diagnosis. Methods: This study proposes an automatic liver segmentation approach based on appearance and context information. Results: The proposed approach is evaluated using CT images with eight contemporary approaches, and it achieves the highest VOE, RVD, ASD, RMSD and MSD. It achieves a high average score of 76 using the MICCAI-2007 Grand Challenge scoring system. Peng et al [5] proposed a convex variational model for liver semi-automatic segmentation in which gradient, local context-based edge detector, and regional appearance constraint are integrated to enhance the liver detection. A geodesic distancebased appearance selection is introduced, and the energy functions incorporating both
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