3D Intelligent Scissors for Dental Mesh Segmentation

Teeth segmentation is a crucial technologic component of the digital dentistry system. The limitations of the live-wire segmentation include two aspects: (1) computing the wire as the segmentation boundary is time-consuming and (2) a great deal of interactions for dental mesh is inevitable. For overcoming these disadvantages, 3D intelligent scissors for dental mesh segmentation based on live-wire is presented. Two tensor-based anisotropic metrics for making wire lie at valleys and ridges are defined, and a timesaving anisotropic Dijkstra is adopted. Besides, to improve with the smoothness of the path tracking back by the traditional Dijkstra, a 3D midpoint smoothing algorithm is proposed. Experiments show that the method is effective for dental mesh segmentation and the proposed tool outperforms in time complexity and interactivity.

RESEARCH ON METHOD OF INTERACTIVE SEGMENTATION BASED ON REMOTE SENSING IMAGES

In this paper, we aim to solve the object extraction problem in remote sensing images using interactive segmentation tools. Firstly, an overview of the interactive segmentation algorithm is proposed. Then, our detailed implementation of intelligent scissors and GrabCut for remote sensing images is described. Finally, several experiments on different typical features (water area, vegetation) in remote sensing images are performed respectively. Compared with the manual result, it indicates that our tools maintain good feature boundaries and show good performance.

Live-Wire-ing the Insight Toolkit with Intelligent Scissors

Semi-automatic image segmentation algorithms depend on interaction with the user to accurately define a region of interest within an image. Once such method is a dynamic programming approach called {em Intelligent Scissors} developed by Mortenson and Barret cite{Mortensen1992,Mortensen1995,Barrett1996,Barrett1997}. Standard interaction involves the user-placement of a seed point on or near the boundary of the object to be extracted. Using a gradient-based cost function, a {em live-wire} image path from the seed point to a subsequently placed boundary point is determined. As this free point is manipulated with the mouse cursor, the live-wire boundary, which extends from the seed point to the varying free point, locks onto nearby edges within the image. Although some applications do not require such user-interaction (e.g. cite{Hu2001}) warranting inclusion within the toolkit, incorporating live-wire capabilities into ITK-SNAP cite{Yushkevich2006} has been discussed and, therefore, we would like to, as a preliminary step, vet the code within the ITK community.