scholarly journals Edge Detection and Level Set Active Contour Model for the Segmentation of Cavity Present in Dental X-Ray Images

2014 ◽  
Vol 96 (9) ◽  
pp. 24-29 ◽  
Author(s):  
Akanksha Kaushik ◽  
Parkash C Mathpal ◽  
Vandini Sharma
Keyword(s):  
Edge Detection ◽  
Level Set ◽  
Active Contour ◽  
Active Contour Model ◽  
X Ray ◽  
Contour Model

scholarly journals Bone fracture detection using active contour model with prior shape

2021 ◽  
Author(s):  
Yun Jia
Keyword(s):  
Active Contour ◽  
Active Contour Model ◽  
Morphological Operations ◽  
Segmentation Method ◽  
Fracture Detection ◽  
Cast Material ◽  
X Ray ◽  
Detection Tool ◽  
Contour Model ◽  
Prior Shape

In this research, an image segmentation method based on active contouring model was studied, which incorporates the prior shape into the active contour evolving process as the global constraint. The active contour model is implemented based on the level set method. The prior shape regulates the behavior of the active contour and keeps it from leaking out of the weak edges. The goal of this research is to determine the displacement and alignment between two fractured pieces of a bone which is encased in the cast material by segmenting them out and calculating their axes difference. The noise introduced by the cast material makes this task difficult. Morphological operations of dilation and erosion are deployed in this research as the noise reduction and edge detection tool. Experiment results are obtained successfully by applying this method upon the X-ray images of patients' fractured arm.

scholarly journals Robust Active Contour Model Guided by Local Binary Pattern Stopping Function

2017 ◽  
Vol 17 (4) ◽  
pp. 165-182 ◽  
Author(s):  
Abdallah Azizi ◽  
Kaouther Elkourd ◽  
Zineb Azizi
Keyword(s):  
Level Set ◽  
Active Contour ◽  
Local Binary Pattern ◽  
Active Contour Model ◽  
Intensity Variation ◽  
Level Set Function ◽  
Contour Model ◽  
Local Intensity ◽  
Edge Based ◽  
Set Function

AbstractEdge based active contour models are adequate to some extent in segmenting images with intensity inhomogeneity but often fail when applied to images with poorly defined or noisy boundaries. Instead of the classical and widely used gradient or edge stopping function which fails to stop contour evolution at such boundaries, we use local binary pattern stopping function to construct a robust and effective active contour model for image segmentation. In fact, comparing to edge stopping function, local binary pattern stopping function accurately distinguishes object’s boundaries and determines the local intensity variation dint to the local binary pattern textons used to classify the image regions. Moreover, the local binary pattern stopping function is applied using a variational level set formulation that forces the level set function to be close to a signed distance function to eliminate costly re-initialization and speed up the motion of the curve. Experiments on several gray level images confirm the advantages and the effectiveness the proposed model.

scholarly journals An Active Contour Model Based on Adaptive Threshold for Extraction of Cerebral Vascular Structures

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Jiaxin Wang ◽  
Shifeng Zhao ◽  
Zifeng Liu ◽  
Yun Tian ◽  
Fuqing Duan ◽  
...  
Keyword(s):  
Level Set Method ◽  
Level Set ◽  
Active Contour ◽  
Active Contour Model ◽  
Boundary Term ◽  
Cerebral Vessel ◽  
Penalty Term ◽  
Level Set Function ◽  
Contour Model ◽  
Global Threshold

Cerebral vessel segmentation is essential and helpful for the clinical diagnosis and the related research. However, automatic segmentation of brain vessels remains challenging because of the variable vessel shape and high complex of vessel geometry. This study proposes a new active contour model (ACM) implemented by the level-set method for segmenting vessels from TOF-MRA data. The energy function of the new model, combining both region intensity and boundary information, is composed of two region terms, one boundary term and one penalty term. The global threshold representing the lower gray boundary of the target object by maximum intensity projection (MIP) is defined in the first-region term, and it is used to guide the segmentation of the thick vessels. In the second term, a dynamic intensity threshold is employed to extract the tiny vessels. The boundary term is used to drive the contours to evolve towards the boundaries with high gradients. The penalty term is used to avoid reinitialization of the level-set function. Experimental results on 10 clinical brain data sets demonstrate that our method is not only able to achieve better Dice Similarity Coefficient than the global threshold based method and localized hybrid level-set method but also able to extract whole cerebral vessel trees, including the thin vessels.

Texture Segmentation of Natural Images Based on Active Contour Model

2012 ◽  
Vol 546-547 ◽  
pp. 553-558
Author(s):  
Zhan Wang ◽  
Yun Hui Yan ◽  
De Wei Dong ◽  
Ke Chen Song
Keyword(s):  
Level Set ◽  
Active Contour ◽  
Active Contour Model ◽  
Texture Synthesis ◽  
Texture Features ◽  
Texture Segmentation ◽  
Natural Images ◽  
Contour Model ◽  
Signal Features ◽  
Environmental Image

To segment complex texture natural environment images; the first, the texture features of natural images should be analysed and the texture features should be extracted; The second, texture images segmengtation can be achieved by using Mumford-Shah active contour model, this segmentation model can better process fuzzy, default boundary, and this model can be solved by level set method. This method can express well complex texture signal features of natural images. Through making texture segmentation experiment for standard texture synthesis image and natural environmental image, its results show that the texture segmentation based on Mumford-Shah active contour model can segment natural images.

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