Survey on liver CT image segmentation methods <s> Graph based semi-automatic methods <s> Abstract Live-wire segmentation is a new interactive tool for efficient, accurate and reproducible boundary extraction which requires minimal user input with a mouse. Optimal boundaries are computed and selected at interactive rates as the user moves the mouse starting from a manually specified seed point. When the mouse position comes into the proximity of an object edge, a ‘live-wire’ boundary snaps to, and wraps around the object of interest. The input of a new seed point ‘freezes’ the selected boundary segment and the process is repeated until the boundary is complete. Two novel enhancements to the basic live-wire methodology include boundary cooling and on-the-fly training. Data-driven boundary cooling generates seed points automatically and further reduces user input. On-the-fly training adapts the dynamic boundary to edges of current interest. Using the live-wire technique, boundaries are extracted in one-fifth of the time required for manual tracing, but with 4.4 times greater accuracy and 4.8 times greater reproducibility. In particular, interobserver reproducibility using the live-wire tool is 3.8 times greater than intraobserver reproducibility using manual tracing. <s> BIB001 </s> Survey on liver CT image segmentation methods <s> Graph based semi-automatic methods <s> Combinatorial graph cut algorithms have been successfully applied to a wide range of problems in vision and graphics. This paper focusses on possibly the simplest application of graph-cuts: segmentation of objects in image data. Despite its simplicity, this application epitomizes the best features of combinatorial graph cuts methods in vision: global optima, practical efficiency, numerical robustness, ability to fuse a wide range of visual cues and constraints, unrestricted topological properties of segments, and applicability to N-D problems. Graph cuts based approaches to object extraction have also been shown to have interesting connections with earlier segmentation methods such as snakes, geodesic active contours, and level-sets. The segmentation energies optimized by graph cuts combine boundary regularization with region-based properties in the same fashion as Mumford-Shah style functionals. We present motivation and detailed technical description of the basic combinatorial optimization framework for image segmentation via s/t graph cuts. After the general concept of using binary graph cut algorithms for object segmentation was first proposed and tested in Boykov and Jolly (2001), this idea was widely studied in computer vision and graphics communities. We provide links to a large number of known extensions based on iterative parameter re-estimation and learning, multi-scale or hierarchical approaches, narrow bands, and other techniques for demanding photo, video, and medical applications. <s> BIB002 </s> Survey on liver CT image segmentation methods <s> Graph based semi-automatic methods <s> A stretchable knitted article of predominately terry loop construction, e.g., an athletic headband, with a design printed thereon, includes a knitted fabric of single jersey construction the reverse side of which has a predominant terry loop region bordering an integrally knit smaller reverse-side plain jersey region. The plain jersey region is oriented in the article for coursewise stretching such that the sinker loop and needle loop crests of the yarn thereof are substantially the only visible yarn portions in both the stretched and unstretched conditions of the article. The design is printed on the plain region whereby it maintains its definition and continuity of appearance when stretched in ordinary use. <s> BIB003
Images are treated as weighted and undirected graphs, where pixels/voxels present the vertices, and neighbor pixels/voxels are considered as connected vertices. The weights of the edges measure the similarity between two connected vertices. The user intervention is employed in the graph methods by the seed selection operation and by the refinement steps. Generally, algorithms under this class are live wire algorithms and graph-cut segmentation algorithms. BIB001 proposed a live wire segmentation algorithm to extract boundaries in medical images. The algorithm computes the smallest cost paths between seed points predefined by the user. Image features such as the gray value, gradient direction; gradient value and Laplacian zero-crossing are used to compute the costs as a weighted sum of these features. First, the user will select an initial seed point on the boundary of the organ, then Dijkstras search algorithm or dynamic programming algorithm will be employed to compute all potential minimum-cost paths, starting from the selected seed point to the rest points in the image. The desired boundary is selected by the user interactively.  extend the live wire method for liver segmentation in CT images and reduce the user interaction and computation time. The cost function is computed locally by considering the liver contour from the nearest adjacent slice already segmented. Live wire segmentation algorithm support the user with a fully control of the segmentation process. The user job will be limited by selecting the seed points and choosing the desired boundary, while the computer will tack care of the details. BIB003 employed graph cut segmentation algorithm in their research. They proposed 3D interactive liver segmentation approach using CT images based on the graph cut segmentation method suggested by BIB002 and two refinement steps. The method is initialized as following; one (or more) seed regions of the liver are marked by the user, the seeds of background are set automatically. Costs calculation for the graph-cut is based on a boundary term and a region term. As a result, the segmented 3D image volume will be presented as a collection of chunks. The first refinement step is employed by asking the user to add or take off individual pieces to or from the segmentation result. Afterwards, the second refinement step is employed where the liver binary mask is converted to a simple mesh which can be refined by different tools.