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%.

scholarly journals Extracting Building Boundaries from High Resolution Optical Images and LiDAR Data by Integrating the Convolutional Neural Network and the Active Contour Model

2018 ◽  
Vol 10 (9) ◽  
pp. 1459 ◽  
Author(s):  
Ying Sun ◽  
Xinchang Zhang ◽  
Xiaoyang Zhao ◽  
Qinchuan Xin
Keyword(s):  
High Resolution ◽  
Active Contour ◽  
Active Contour Model ◽  
Remote Sensing Data ◽  
Lidar Data ◽  
Boundary Extraction ◽  
Contour Model ◽  
Optical Images ◽  
Building Footprint ◽  
Salt And Pepper

Identifying and extracting building boundaries from remote sensing data has been one of the hot topics in photogrammetry for decades. The active contour model (ACM) is a robust segmentation method that has been widely used in building boundary extraction, but which often results in biased building boundary extraction due to tree and background mixtures. Although the classification methods can improve this efficiently by separating buildings from other objects, there are often ineluctable salt and pepper artifacts. In this paper, we combine the robust classification convolutional neural networks (CNN) and ACM to overcome the current limitations in algorithms for building boundary extraction. We conduct two types of experiments: the first integrates ACM into the CNN construction progress, whereas the second starts building footprint detection with a CNN and then uses ACM for post processing. Three level assessments conducted demonstrate that the proposed methods could efficiently extract building boundaries in five test scenes from two datasets. The achieved mean accuracies in terms of the F1 score for the first type (and the second type) of the experiment are 96.43 ± 3.34% (95.68 ± 3.22%), 88.60 ± 3.99% (89.06 ± 3.96%), and 91.62 ±1.61% (91.47 ± 2.58%) at the scene, object, and pixel levels, respectively. The combined CNN and ACM solutions were shown to be effective at extracting building boundaries from high-resolution optical images and LiDAR data.

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 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.

scholarly journals Edge Enhancement Based Active Contour Model for Segmentation of Brain Tumor in MRI Images

2021 ◽  
Vol 19 (3) ◽  
Author(s):  
Mustafa Rashid Ismael
Keyword(s):  
Edge Detection ◽  
Active Contour ◽  
Active Contour Model ◽  
Initial Contour ◽  
Morphological Operations ◽  
Significant Information ◽  
Automated Method ◽  
Contour Model ◽  
Tumor Region ◽  
Edge Sharpening

Tumor segmentation is one of the most significant tasks in brain image analysis due to the significant information obtained by the tumor region. Therefore, many methods have been proposed during the last two decades for segmenting the tumor in MRI images. In this paper, an automated method is proposed using an active contour model with an initial contour creation using edge sharpening, thresholding, and morphological operations. Four methods of edge detection are utilized in the edge sharpening process (Sobel, Roberts, Prewitt, and Canny) and their performance was investigated in terms of Dice, Jaccard, and F1 score. The experiments were implemented on BRATS datasets with both HGG and LGG images. The study indicates that sharpening the edges using edge detection is essential to improve the segmentation of the tumor region especially when it is used with an active contour model. The achieved results show the effectiveness of the proposed method and it outperformed some recent existing methods.

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.

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