Extraction of the Borderline in Prepared Tooth Cavity Based on Intelligent Scissors

2008 ◽  
Author(s):  
Shuxian Zheng ◽  
Jia Li ◽  
Qingfeng Sun
Keyword(s):  
Intelligent Scissors

scholarly journals 3D Intelligent Scissors for Dental Mesh Segmentation

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Shuai Yang ◽  
Ruikun Wang ◽  
Wenjie Zhao ◽  
Yongzhen Ke
Keyword(s):  
Time Complexity ◽  
Path Tracking ◽  
Mesh Segmentation ◽  
Smoothing Algorithm ◽  
Digital Dentistry ◽  
Segmentation Boundary ◽  
The Wire ◽  
Intelligent Scissors

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.

scholarly journals Live-Wire-ing the Insight Toolkit with Intelligent Scissors

2009 ◽  
Author(s):  
Nicholas J. Tustison ◽  
Paul Yushkevich ◽  
James Gee
Keyword(s):  
User Interaction ◽  
Region Of Interest ◽  
Programming Approach ◽  
Seed Point ◽  
Preliminary Step ◽  
Dynamic Programming Approach ◽  
Mouse Cursor ◽  
Segmentation Algorithms ◽  
Gradient Based ◽  
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.

INTERACTIVE NAVIGATION OF VIRTUAL VESSEL TRACKING WITH 3D INTELLIGENT SCISSORS

2001 ◽  
Vol 01 (02) ◽  
pp. 273-285
Author(s):  
PHENG-ANN HENG ◽  
HANQIU SUN ◽  
KWONG-WAI CHEN ◽  
TIEN-TSIN WONG
Keyword(s):  
Three Dimensional ◽  
Clear Understanding ◽  
Volume Data ◽  
Interactive Control ◽  
Vessel Morphology ◽  
Vessel Tracking ◽  
Fully Automatic ◽  
Interactive Navigation ◽  
User Intervention ◽  
Intelligent Scissors

Vessel tracking is usually needed for various medical usages such as clinical diagnosis and surgery planning. A clear understanding of the patient's actual vessel morphology is very important in planning the surgery. Tracking the vessels/bronchia from scanned medical images such as CT or MRI manually is very tedious and labor-intensive. On the other hand, a fully automatic tracking algorithm may not be very reliable. It may be sensitive to the noise in the volume data. In this paper, we propose a vessel tracking method that finds air-way paths using three-dimensional intelligent scissors. 3D Intelligent Scissors has the ability to extract hollow vessels by climbing along surfaces. Thus, a rough path can be found if the end points of a narrow tunnel are selected. Another advantage of using 3D Intelligent Scissors is that it can successfully extract the path even the actual path is broken. Very often the diameter of a blood vessel may be sometimes smaller than one voxel. Since our algorithm is semi-automatic, it provides sufficient control to accurately find out the desired paths with only minimal user intervention. We have applied the proposed 3D Intelligent Scissors for the application of tracking lung air-ways. A 3D stylus and cutting planes in the virtual volume space are used for selecting the end nodes of vessel paths and revealing the local details on the texture-mapped plane. Our system also provides advanced navigation features to guide visual examination and diagnosis of volumetric air-way paths. These include interactive control of incremental forward, backward, and rotating navigation in reference to the viewing frustum and 3D real-time input.

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