Interactive segmentation method with graph cut and SVMs

2010 ◽  
Author(s):  
Xing Zhang ◽  
Jie Tian ◽  
Dehui Xiang ◽  
Yongfang Wu
Keyword(s):  
Graph Cut ◽  
Segmentation Method ◽  
Interactive Segmentation

An Interactive Video Foreground Segmentation System Based on Modeling and Dynamic Graph Cut Algorithm

2012 ◽  
Vol 532-533 ◽  
pp. 1770-1774
Author(s):  
Xiao Yu Wu ◽  
Lei Yang ◽  
Shao Bin Li ◽  
Pin Xu
Keyword(s):  
Interactive Video ◽  
Video Sequence ◽  
User Interaction ◽  
Graph Cut ◽  
Foreground Segmentation ◽  
Segmentation Method ◽  
Dynamic Graph ◽  
Interactive Segmentation ◽  
User Interactions ◽  
Current Frame

This paper proposes an interactive video foreground segmentation method based on modeling and graph cut algorithm. User interactions are required at initial frame or key frame of video sequence at first. Secondly we make use of user interactions information to develop background/foreground model and get foreground segmentation result of the current frame in term of graph cut algorithm. And automatic updated methods are proposed to obtain foreground segmentation results automatically on the later sequence of video without user interaction. The developed system of interactive video foreground segmentation has performances with extracting object and editing segmentation results. Experimental results on kinds of video demonstrated that our interactive segmentation system is efficient.

GRAPH CUT SEGMENTATION METHOD AND ITS APPLICATION IN MEDICAL IMAGES

2015 ◽  
Vol 75 (4) ◽  
Author(s):  
Alireza Norouzi ◽  
Ismail Mat Amin ◽  
Mohd Shafry Mohd Rahim ◽  
Abdolvahab Ehsani Rad
Keyword(s):  
Cost Function ◽  
Medical Images ◽  
Graph Cut ◽  
Segmentation Method ◽  
Interactive Segmentation ◽  
Optimum Result ◽  
Image Pixels ◽  
The Cost ◽  
Graph Cut Segmentation ◽  
Min Cut

Graph cut is an interactive segmentation method. It works based on preparing graph from image and finds the minimum cut for the graph. The edges value is calculated based on belonging a pixel to object or background. The advantage of this method is using the cost function. If the cost function is clearly described, graph cut is presents a generally optimum result. In this paper graph concepts and preparing graph according to image pixels is described. Preparing different edges and performing min cut/max flow is explained. Finally, the method is applied on some medical images.  

scholarly journals Interactive Segmentation of Pancreases in Abdominal Computed Tomography Images and Its Evaluation Based on Segmentation Accuracy and Interaction Costs

2017 ◽  
Vol 2017 ◽  
pp. 1-8 ◽  
Author(s):  
Hotaka Takizawa ◽  
Takenobu Suzuki ◽  
Hiroyuki Kudo ◽  
Toshiyuki Okada
Keyword(s):  
Computed Tomography ◽  
Ct Images ◽  
Graph Cut ◽  
Medical Doctor ◽  
Seed Selection ◽  
Segmentation Method ◽  
Interactive Segmentation ◽  
Abdominal Computed Tomography ◽  
Computed Tomography Images ◽  
Interaction Costs

The present paper proposed an interactive segmentation method of pancreases in abdominal computed tomography (CT) images based on the anatomical knowledge of medical doctors and the statistical information of pancreas shapes. This segmentation method consisted of two phases: training and testing. In the training phase, pancreas regions were manually extracted from sample CT images for training, and then a probabilistic atlas (PA) was constructed from the extracted regions. In the testing phase, a medical doctor selected seed voxels for a pancreas and background in a CT image for testing by use of our graphical user interface system. The homography transformation was used to fit the PA to the seeds. The graph cut technique whose data term was weighted by the transformed PA was applied to the test image. The seed selection, the atlas transformation, and the graph cut were executed iteratively. This doctor-in-the-loop segmentation method was applied to actual abdominal CT images of fifteen cases. The experimental results demonstrated that the proposed method was more accurate and effective than the conventional graph cut.

scholarly journals An Interactive Segmentation Method Based on Superpixel

2015 ◽  
Vol 22 ◽  
pp. 01027
Author(s):  
Shu Yang ◽  
Yaping Zhu ◽  
Xiaoyu Wu
Keyword(s):  
Segmentation Method ◽  
Interactive Segmentation
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