Pattern Spectrum and Multiscale Shape Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern recognition and image analysis
Pattern recognition and image analysis
Interactive segmentation with Intelligent Scissors
Graphical Models and Image Processing
Digital Image Processing
Multiscale morphological segmentation of gray-scale images
IEEE Transactions on Image Processing
IEEE Transactions on Circuits and Systems for Video Technology
Liver Segmentation from CT Scans: A Survey
WILF '07 Proceedings of the 7th international workshop on Fuzzy Logic and Applications: Applications of Fuzzy Sets Theory
Liver segmentation from computed tomography scans: A survey and a new algorithm
Artificial Intelligence in Medicine
Survey on liver CT image segmentation methods
Artificial Intelligence Review
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Automatic liver segmentation from abdominal computed tomography (CT) images is one of the most important steps for computer-aided diagnosis (CAD) for liver CT. However, the liver must be separated manually or semi-automatically since surface features of the liver and partial-volume effects make automatic discrimination from other adjacent organs or tissues very difficult. In this paper, we present an unsupervised liver segmentation algorithm with three steps. In the preprocessing, we simplify the input CT image by estimating the liver position using a prior knowledge about the location of the liver and by performing multilevel threshold on the estimated liver position. The proposed scheme utilizes the multiscale morphological filter recursively with region-labeling and clustering to detect the search range for deformable contouring. Most of the liver contours are positioned within the search range. In order to perform an accurate segmentation, we produce the gradient-label map, which represents the gradient magnitude in the search range. The proposed algorithm performed deformable contouring on the gradient-label map by using regular patterns of the liver boundary. Experimental results are comparable to those of manual tracing by radiological doctors and shown to be efficient.