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 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 A Probabilistic Approach for Breast Boundary Extraction in Mammograms

2013 ◽  
Vol 2013 ◽  
pp. 1-19 ◽  
Author(s):  
Hamed Habibi Aghdam ◽  
Domenec Puig ◽  
Agusti Solanas
Keyword(s):  
Active Contour ◽  
Active Contour Model ◽  
Probability Model ◽  
Probabilistic Approach ◽  
Local Binary Patterns ◽  
Extraction Process ◽  
Morphological Operations ◽  
Boundary Extraction ◽  
Contour Model ◽  
Starting Point

The extraction of the breast boundary is crucial to perform further analysis of mammogram. Methods to extract the breast boundary can be classified into two categories: methods based on image processing techniques and those based on models. The former use image transformation techniques such as thresholding, morphological operations, and region growing. In the second category, the boundary is extracted using more advanced techniques, such as the active contour model. The problem with thresholding methods is that it is a hard to automatically find the optimal threshold value by using histogram information. On the other hand, active contour models require defining a starting point close to the actual boundary to be able to successfully extract the boundary. In this paper, we propose a probabilistic approach to address the aforementioned problems. In our approach we use local binary patterns to describe the texture around each pixel. In addition, the smoothness of the boundary is handled by using a new probability model. Experimental results show that the proposed method reaches 38% and 50% improvement with respect to the results obtained by the active contour model and threshold-based methods respectively, and it increases the stability of the boundary extraction process up to 86%.

An Image Segmentation Method of Underwater Targets Based on Active Contour Model

2014 ◽  
Vol 511-512 ◽  
pp. 457-461
Author(s):  
Tao Liu ◽  
Lei Wan ◽  
Xing Wei Liang
Keyword(s):  
Image Segmentation ◽  
Active Contour ◽  
Active Contour Model ◽  
Signal To Noise Ratio ◽  
Detection Algorithm ◽  
Segmentation Method ◽  
Contour Model ◽  
Underwater Targets ◽  
Underwater Target ◽  
Images Processing

The underwater images are disturbed with low signal to noise ratio and edge blur, because there are the light scattering and absorption effects. If the traditional thresholding method is used directly to segment underwater images, it will usually lead to be less effective to process underwater images. An image segmentation method of underwater target based on active contour model was proposed in this paper. Firstly, using Canny edge detection algorithm to detect the edges of the original image to obtain the information of a crude outline, then the algorithm based on C-V active contour model to segment underwater target images was addressed. The images processing results based on threshold segmentation method and C-V model method were compared. Experiments demonstrate the effectiveness of the proposed algorithm for underwater targets images segmentation.

scholarly journals The multichannel integration active contour framework for crack detection

2019 ◽  
Vol 16 (3) ◽  
pp. 172988141985285
Author(s):  
Xiaomin Xie ◽  
Yilin Xia ◽  
Bo Liu ◽  
Kui Li ◽  
Tingting Wang
Keyword(s):  
Active Contour ◽  
Crack Detection ◽  
Active Contour Model ◽  
Morphological Operations ◽  
Salient Region Detection ◽  
Nonlocal Means ◽  
Feature Map ◽  
Region Detection ◽  
Contour Model ◽  
Infrastructure Maintenance

Crack is one of the most important defects to evaluate the health of concrete buildings. Hence, accurate detection is of great significance for the infrastructure maintenance. In this article, an efficient multichannel active contour model for crack extraction is proposed, which integrates various features of the cracks. Firstly, the nonlocal means technique is adopted to eliminate the effects of noise while preserving the edge details. Then, the novel multichannel active contour model energy function is constructed, which considers three characteristics of the cracks: (a) the intensity features map, which is on the basis of the distinct intensity of the cracks; (b) the saliency feature map, which is obtained by the frequency-tuned salient region detection; and (c) the line-like feature map, which is enhanced by the multi-scale Hessian filtering. Also, the line-like feature map is binarized by a set of morphological operations and the Otsu thresholding to initialize the active contour. The proposed approach has been compared with the existing detection models on the public database and the real-world cracks. The experimental results show the effectiveness and efficiency of the proposed model.

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