Tumor Boundary Delineation Using Abnormality Outlining Box Guided Modified GVF Snake Model

2019 ◽  
pp. 135-144
Author(s):  
Srinivas Thirumala ◽  
Srinivasa Rao Chanamallu
Keyword(s):  
Snake Model

Repulsive force based snake model to segment and track neuronal axons in 3D microscopy image stacks

NeuroImage ◽  
2006 ◽  
Vol 32 (4) ◽  
pp. 1608-1620 ◽  
Author(s):  
Hongmin Cai ◽  
Xiaoyin Xu ◽  
Ju Lu ◽  
Jeff W. Lichtman ◽  
S.P. Yung ◽  
...  
Keyword(s):  
Repulsive Force ◽  
Snake Model ◽  
3D Microscopy ◽  
Microscopy Image

Detecting the Media-adventitia Border in Intravascular Ultrasound Images through a Classification-based Approach

2018 ◽  
Vol 41 (2) ◽  
pp. 78-93 ◽  
Author(s):  
Yuan-yuan Wang ◽  
Chen-hui Qiu ◽  
Jun Jiang ◽  
Shun-ren Xia
Keyword(s):  
Intravascular Ultrasound ◽  
Feature Detection ◽  
Guide Wire ◽  
Disease Diagnosis ◽  
Rectangular Domain ◽  
Ultrasound Images ◽  
Snake Model ◽  
External Force Field ◽  
The Media ◽  
Learning Machine

The detection of the media-adventitia (MA) border in intravascular ultrasound (IVUS) images is essential for vessel assessment and disease diagnosis. However, it remains a challenging task, considering the existence of plaque, calcification, and various artifacts. In this article, an effective method based on classification is proposed to extract the MA border in IVUS images. First, a novel morphologic feature describing the relative position of each structure relative to the MA border, called RPES for short, is proposed. Then, the RPES feature and other features are employed in a multiclass extreme learning machine (ELM) to classify IVUS images into nine classes including the MA border and other structures. At last, a modified snake model is employed to effectively detect the MA border in the rectangular domain, in which a modified external force field is constructed on the basis of local border appearances and classification results. The proposed method is evaluated on a public dataset with 77 IVUS images by three indicators in eight situations, such as calcification and a guide wire artifact. With the proposed RPES feature, detection performances are improved by more than 39 percent, which shows an apparent advantage in comparative experiments. Furthermore, compared with two other existing methods used on the same dataset, the proposed method achieves 18 of the best indicators among 24, demonstrating its higher capability in detecting the MA border.

scholarly journals AN IMPROVED SNAKE MODEL FOR REFINEMENT OF LIDAR-DERIVED BUILDING ROOF CONTOURS USING AERIAL IMAGES

2016 ◽  
Vol XLI-B3 ◽  
pp. 583-589
Author(s):  
Qi Chen ◽  
Shugen Wang ◽  
Xiuguo Liu
Keyword(s):  
Urban Areas ◽  
Quality Index ◽  
Effective Means ◽  
Urban Air Pollution ◽  
High Elevation ◽  
Aerial Images ◽  
Snake Model ◽  
Building Roof ◽  
Searching Strategy ◽  
High Resolution Imagery

Building roof contours are considered as very important geometric data, which have been widely applied in many fields, including but not limited to urban planning, land investigation, change detection and military reconnaissance. Currently, the demand on building contours at a finer scale (especially in urban areas) has been raised in a growing number of studies such as urban environment quality assessment, urban sprawl monitoring and urban air pollution modelling. LiDAR is known as an effective means of acquiring 3D roof points with high elevation accuracy. However, the precision of the building contour obtained from LiDAR data is restricted by its relatively low scanning resolution. With the use of the texture information from high-resolution imagery, the precision can be improved. In this study, an improved snake model is proposed to refine the initial building contours extracted from LiDAR. First, an improved snake model is constructed with the constraints of the deviation angle, image gradient, and area. Then, the nodes of the contour are moved in a certain range to find the best optimized result using greedy algorithm. Considering both precision and efficiency, the candidate shift positions of the contour nodes are constrained, and the searching strategy for the candidate nodes is explicitly designed. The experiments on three datasets indicate that the proposed method for building contour refinement is effective and feasible. The average quality index is improved from 91.66% to 93.34%. The statistics of the evaluation results for every single building demonstrated that 77.0% of the total number of contours is updated with higher quality index.

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