Erratum: Enhanced X‐ray image segmentation method using prior shape

2017 ◽  
Vol 11 (6) ◽  
pp. 505-505
Keyword(s):  
Image Segmentation ◽  
Segmentation Method ◽  
X Ray ◽  
Prior Shape

Enhanced X‐ray image segmentation method using prior shape

2016 ◽  
Vol 11 (2) ◽  
pp. 145-152 ◽  
Author(s):  
Hossein Mahvash Mohammadi ◽  
Jacques A. Guise
Keyword(s):  
Image Segmentation ◽  
Segmentation Method ◽  
X Ray ◽  
Prior Shape

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 Cancer Detection from X-Ray and MRI Images through Image Segmentation Techniques

2020 ◽  
Vol 8 (6) ◽  
pp. 273-278
Keyword(s):  
Image Segmentation ◽  
Cancer Detection ◽  
Image Interpretation ◽  
Bone Cancer ◽  
Region Growing ◽  
Segmentation Method ◽  
X Ray ◽  
Science Paper ◽  
Biomedical Field ◽  
Edge Based

This paper is based on integration of the biomedical field and computer science. Paper contains the study of bone cancer and features to predict the type of the same. Related work to find cancer in human body using computer vision is discussed in this paper. Image segmentation technique like sobel, prewitt, canny, K-means and Region Growing are described in this paper which can be stimulated for X-Ray and MRI image interpretation. Paper also shows the result of edge based and region based image segmentation techniques applied on X-Ray image to detect osteosarcoma cancer present on bone using MATLAB. Finally, paper concluded by finding best suited segmentation method for grey scaled image with future aspects.

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.

An Effective X-Ray Image Segmentation Method for the Pharmaceutical Industry via Surface Fitting

2013 ◽  
Vol 333-335 ◽  
pp. 839-844
Author(s):  
Kai Hong Shi ◽  
Zong Qing Lu ◽  
Qing Min Liao
Keyword(s):  
Image Segmentation ◽  
Pharmaceutical Industry ◽  
Active Contour Model ◽  
Three Dimensional ◽  
Surface Fitting ◽  
Segmentation Method ◽  
Image Subtraction ◽  
Low Contrast ◽  
X Ray ◽  
Fixed Condition

Image segmentation techniques currently used for X-ray inspection in pharmaceutical industry suffer from some limitations. The object in an image is close to the background and its contours are weak or blurred because of the X-ray imaging characteristic. Based on our research of X-ray inspection, a simple and efficient image segmentation method is proposed in this paper. It is implemented by treating the image and desired contours as three dimensional surface and holes respectively in order to simplify the model of segmentation, and making use of surface fitting and image subtraction to extract the target region efficiently. The novelty of this approach is that we need less selection of parameters to extract contours with low contrast by surface fitting. Experiments on real X-ray images demonstrate the advantages of the proposed method over active contour model (ACM) and Chan_Vese model (CV model) in terms of both accuracy and efficiency on fixed condition.

Multiresolution Bio-Medical Image Segmentation using Fuzzy C-Means Clustering

2019 ◽  
Vol 8 (4) ◽  
pp. 9548-9551
Keyword(s):  
Image Segmentation ◽  
Medical Image Segmentation ◽  
Experimental Result ◽  
Segmentation Method ◽  
Fuzzy C Means ◽  
Fuzzy C Means Clustering ◽  
Robust Image ◽  
Multiple Clusters ◽  
Single Pixel ◽  
Popular Image

Fuzzy c-means clustering is a popular image segmentation technique, in which a single pixel belongs to multiple clusters, with varying degree of membership. The main drawback of this method is it sensitive to noise. This method can be improved by incorporating multiresolution stationary wavelet analysis. In this paper we develop a robust image segmentation method using Fuzzy c-means clustering and wavelet transform. The experimental result shows that the proposed method is more accurate than the Fuzzy c-means clustering.

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