A New Contour Initialization of CT Image Sequences in GVF Model

2013 ◽  
Vol 321-324 ◽  
pp. 1225-1229
Author(s):  
Jie Zhao ◽  
Fa Ling Yi ◽  
Zhan Peng Huang
Keyword(s):  
Cancer Detection ◽  
Image Sequences ◽  
Initial Contour ◽  
Watershed Algorithm ◽  
Segmentation Result ◽  
Ct Image ◽  
Gray Scale ◽  
Snake Model ◽  
Serial Images ◽  
Initialization Algorithm

In order to overcome the shortcomings that GVF model is susceptible to structures with slender topology, an improved watershed algorithm is proposed to determine initial contour of GVF Snake model. The organs in medical CT images often were irregular and had deep boundary concavities, and CT serial images were up to hundreds pieces. Firstly, in a CT image the improved watershed algorithm grows an organ from a seed block by the principle of the similarity between gray scale and texture, then using its edge as the start contour of the adjacent CT sequence, with GVF algorithm segment the organ from a sequence of images, the process is repeated until get all slices of the entire abdomen. Experiment results which are the basic of 3-D reconstruction and cancer detection show that the new contour initialization algorithm can obtain segmentation result efficiently, accurately and cost less time.

scholarly journals Liver Segmentation Based on Snakes Model and Improved GrowCut Algorithm in Abdominal CT Image

2013 ◽  
Vol 2013 ◽  
pp. 1-12 ◽  
Author(s):  
Huiyan Jiang ◽  
Baochun He ◽  
Zhiyuan Ma ◽  
Mao Zong ◽  
Xiangrong Zhou ◽  
...  
Keyword(s):  
Ct Images ◽  
Experimental Results ◽  
Initial Contour ◽  
Segmentation Result ◽  
Ct Image ◽  
Liver Segmentation ◽  
Abdominal Ct ◽  
Running Time ◽  
Novel Method ◽  
Liver Region

A novel method based on Snakes Model and GrowCut algorithm is proposed to segment liver region in abdominal CT images. First, according to the traditional GrowCut method, a pretreatment process using K-means algorithm is conducted to reduce the running time. Then, the segmentation result of our improved GrowCut approach is used as an initial contour for the future precise segmentation based on Snakes model. At last, several experiments are carried out to demonstrate the performance of our proposed approach and some comparisons are conducted between the traditional GrowCut algorithm. Experimental results show that the improved approach not only has a better robustness and precision but also is more efficient than the traditional GrowCut method.

Improved Snake Model Algorithm with Application in Liver Image Segmentation

2012 ◽  
Vol 263-266 ◽  
pp. 2643-2648 ◽  
Author(s):  
Hong Lan ◽  
Le Quan Min
Keyword(s):  
Image Segmentation ◽  
Active Contour ◽  
Initial Contour ◽  
Watershed Algorithm ◽  
Curve Shape ◽  
Mr Images ◽  
Snake Model ◽  
Contour Models ◽  
Model Algorithm ◽  
Liver Image

Snake model is one of classical active contour models, and is widely used in the image segmentation. But it has defects of sensitive to the initial contour and lack of curvature constraints. This paper presents an improved Snake model algorithm, which initializes contour by maker control watershed algorithm and adds an external force related with the curve shape into the energy function of Snake model to solve the problem about curvature constraints. Testing on the Magnetic Resonance (MR) images of the abdomen for liver image segmentation proves that the new algorithm can achieve good results.

Semi-Automatic Liver Segmentation Using Improved GVF Snake Model

2010 ◽  
Vol 121-122 ◽  
pp. 435-440 ◽  
Author(s):  
Hui Yan Jiang ◽  
Xi He Gao
Keyword(s):  
Force Field ◽  
External Force ◽  
Left Lobe ◽  
Initial Contour ◽  
Ct Image ◽  
Liver Segmentation ◽  
Threshold Method ◽  
Abdominal Ct ◽  
Snake Model ◽  
External Force Field

Snakes are extensively used in computer vision and Image processing. However, when it comes to the liver segmentation from computed tomography (CT) image, the application of the models is limited because it can not extend to certain boundary indentations of the liver. In order to solve this problem, we developed an improved GVF snake model by adding an external force field which can efficiently attract the initial contour to these depression areas, such as the top of the left lobe of liver. The proposed method includes two steps. Firstly, combined with the threshold method and the morphology operation, our model can acquire the initial contour of the liver. Secondly, we create an imposed external force field through the interaction with the system, and we make the initial contour converge under the influence of both GVF field and imposed external force field to get the accurate contour of the liver. The application of this method on abdominal CT image is demonstrated, both qualitatively and quantitatively.

Automatic Segmentation of the Skull in MRI Sequences Using Level Set Method

2011 ◽  
Vol 58-60 ◽  
pp. 2370-2375
Author(s):  
Wei Li Ding ◽  
Feng Jiang ◽  
Jia Qing Yan
Keyword(s):  
Level Set ◽  
Medical Image ◽  
Automatic Segmentation ◽  
Image Sequences ◽  
Initial Contour ◽  
Single Image ◽  
Mri Sequences ◽  
Magnetic Resonance Imaging Mri ◽  
Mri Image

Magnetic Resonance Imaging (MRI) has been widely used in clinical diagnose. Segmentation of these images obtained by MRI is a necessary procedure in medical image processing. In this paper, an improved level set algorithm was proposed to optimize the segmentation of MRI image sequences based on article [1]. Firstly, we add an area term and the edge indicator function to the total energy function for single image segmentation. Secondly, we presented a new method which uses the circumscribed polygon of the previous segmentation result as the initial contour of the next image to achieve automatic segmentation of image sequences. The algorithm was tested on MRI image sequences provided by Chuiyanliu Hospital, Chaoyang District of Beijing; the results have indicated that the proposed algorithm can effectively enhance the segmentation speed and quality of MRI sequences.

CT Images Segmentation Based on Dynamic Relative Fuzzy Region Growing Algorithm

2014 ◽  
Vol 644-650 ◽  
pp. 4233-4236
Author(s):  
Zhen You Zhang ◽  
Guo Huan Lou
Keyword(s):  
Confidence Interval ◽  
Region Growing ◽  
Ct Images ◽  
Segmentation Algorithm ◽  
Experimental Results ◽  
Segmentation Result ◽  
Ct Image ◽  
Initial Segmentation ◽  
Fuzzy Region

Segmentation algorithm of CT Image is discussed in this paper. Dynamic relative fuzzy region growing algorithm is used for CT. At the beginning of the segmentation, the confidence interval region growing algorithm is used. The overlapping parts in the initial segmentation result is segmented again with the improved fuzzy connected, and then determine which region the overlapping parts belong to. Thus, the final segmentation result can be obtained. Since the algorithm contains the advantages of region growing algorithm, fuzzy connected algorithm and the region competition, the runtime of segmentation is greatly reduced and better experimental results are obtained.

scholarly journals A Hybrid Method for Pancreas Extraction from CT Image Based on Level Set Methods

2013 ◽  
Vol 2013 ◽  
pp. 1-13 ◽  
Author(s):  
Huiyan Jiang ◽  
Hanqing Tan ◽  
Hiroshi Fujita
Keyword(s):  
Level Set Method ◽  
Level Set ◽  
Negative Impact ◽  
Level Set Methods ◽  
Region Growing ◽  
Ct Images ◽  
Medical Image Segmentation ◽  
Initial Contour ◽  
Ct Image ◽  
Fast Marching

This paper proposes a novel semiautomatic method to extract the pancreas from abdominal CT images. Traditional level set and region growing methods that request locating initial contour near the final boundary of object have problem of leakage to nearby tissues of pancreas region. The proposed method consists of a customized fast-marching level set method which generates an optimal initial pancreas region to solve the problem that the level set method is sensitive to the initial contour location and a modified distance regularized level set method which extracts accurate pancreas. The novelty in our method is the proper selection and combination of level set methods, furthermore an energy-decrement algorithm and an energy-tune algorithm are proposed to reduce the negative impact of bonding force caused by connected tissue whose intensity is similar with pancreas. As a result, our method overcomes the shortages of oversegmentation at weak boundary and can accurately extract pancreas from CT images. The proposed method is compared to other five state-of-the-art medical image segmentation methods based on a CT image dataset which contains abdominal images from 10 patients. The evaluated results demonstrate that our method outperforms other methods by achieving higher accuracy and making less false segmentation in pancreas extraction.

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