A Multi-Scale Information Fusion Level Set for Breast Tumor Segmentation

2021 ◽  
Vol 11 (8) ◽  
pp. 2062-2070
Author(s):  
Tongle Fan ◽  
Guanglei Wang ◽  
Yan Li ◽  
Zhongyang Wang ◽  
Hongrui Wang
Keyword(s):  
Level Set ◽  
Breast Tumor ◽  
Region Of Interest ◽  
Energy Functional ◽  
Surrounding Tissue ◽  
Tumor Segmentation ◽  
Energy Term ◽  
Edge Information ◽  
Breast Segmentation ◽  
Level Set Model

Purpose: Mammography is considered an effective method of examination in early breast cancer screening. Massive work by distinguished researchers of breast segmentation has been proposed. However, due to the blurry boundaries of the breast tumor, the variability of its shape and the overlap with surrounding tissue, the breast tumor’s accurate segmentation still is a challenge. Methods: In this paper, we proposed a novel level set model which based on the optimized local region driven gradient enhanced level set model (OLR-GCV) to segment tumor within a region of interest (ROI) in a mammogram. Firstly, Noise, labels and artifacts are removed from breast images. The ROI is then obtained using the intuitionistic fuzzy C-means method. Finally we used OLR-GCV method to accurately segment the breast tumor. The OLR-GCV model combines regional information, enhanced edge information and optimized Laplacian of Gaussian (LOG) energy term. The regional and enhanced edge information are used to capture local, global and gradient information of breast images. The optimized Laplacian of Gaussian (LOG) energy term is introduced in the energy functional to further optimize edge information to improve segmentation accuracy. Results: We evaluated our method on the MIAS and DDSM datasets. It yielded a Dice value of 96.86% on the former and 95.51% on the latter. Our method proposed achieves higher accuracy of segmentation than other State-of-the-art Methods. Conclusions: Our method has better segmentation performance, and can be used in clinical practice.

scholarly journals Dynamic Regulation of Level Set Parameters Using 3D Convolutional Neural Network for Liver Tumor Segmentation

2019 ◽  
Vol 2019 ◽  
pp. 1-17 ◽  
Author(s):  
Zhuofu Deng ◽  
Qingzhe Guo ◽  
Zhiliang Zhu
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Level Set ◽  
Liver Tumors ◽  
Window Size ◽  
Energy Functional ◽  
Tumor Segmentation ◽  
Dynamic Regulation ◽  
Low Contrast ◽  
3D Cnn

Segmentation of liver tumors plays an important role in the choice of therapeutic strategies for liver disease and treatment monitoring. In this paper, we generalize the process of a level set with a novel algorithm of dynamic regulation to energy functional parameters. The presented method is fully automatic once the tumor has been detected. First, a 3D convolutional neural network with dense layers for classification is used to estimate current contour location relative to the tumor boundary. Second, the output 3D CNN probabilities can dynamically regulate parameters of the level set functional over the process of segmentation. Finally, for full automation, appropriate initializations and local window size are generated based on the current contour position probabilities. We demonstrate the proposed method on the dataset of MICCAI 2017 LiTS Challenge and 3DIRCADb that include low contrast and heterogeneous tumors as well as noisy images. To illustrate the strength of our method, we evaluated it against the state-of-the-art methods. Compared with the level set framework with fixed parameters, our method performed better significantly with an average DICE improvement of 0.15. We also analyzed a challenging dataset 3DIRCADb of tumors and obtained a competitive DICE of 0.85±0.06 with the proposed method.

scholarly journals LSM-SEC: Tongue Segmentation by the Level Set Model with Symmetry and Edge Constraints

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Shanshan Gao ◽  
Ningning Guo ◽  
Deqian Mao
Keyword(s):  
Level Set ◽  
The Other ◽  
Surrounding Tissue ◽  
Geometric Features ◽  
Symmetry Detection ◽  
Qualitative And Quantitative ◽  
Segmentation Methods ◽  
Edge Probability ◽  
Tongue Segmentation ◽  
Level Set Model

Accurate segmentation of the tongue body is an important prerequisite for computer-aided tongue diagnosis. In general, the size and shape of the tongue are very different, the color of the tongue is similar to the surrounding tissue, the edge of the tongue is fuzzy, and some of the tongue is interfered by pathological details. The existing segmentation methods are often not ideal for tongue image processing. To solve these problems, this paper proposes a symmetry and edge-constrained level set model combined with the geometric features of the tongue for tongue segmentation. Based on the symmetry geometry of the tongue, a novel level set initialization method is proposed to improve the accuracy of subsequent model evolution. In order to increase the evolution force of the energy function, symmetry detection constraints are added to the evolution model. Combined with the latest convolution neural network, the edge probability input of the tongue image is obtained to guide the evolution of the edge stop function, so as to achieve accurate and automatic tongue segmentation. The experimental results show that the input tongue image is not subject to the external capturing facility or environment, and it is suitable for tongue segmentation under most realistic conditions. Qualitative and quantitative comparisons show that the proposed method is superior to the other methods in terms of robustness and accuracy.

Ultrasound breast tumor segmentation based on 3-D level set

2007 ◽  
Author(s):  
Li Li ◽  
Ying Wang ◽  
Yue Ou ◽  
Qi Liu
Keyword(s):  
Level Set ◽  
Breast Tumor ◽  
Tumor Segmentation

A Likelihood and Local Constraint Level Set Model for Liver Tumor Segmentation from CT Volumes

2013 ◽  
Vol 60 (10) ◽  
pp. 2967-2977 ◽  
Author(s):  
Changyang Li ◽  
Xiuying Wang ◽  
Stefan Eberl ◽  
Michael Fulham ◽  
Yong Yin ◽  
...  
Keyword(s):  
Level Set ◽  
Liver Tumor ◽  
Tumor Segmentation ◽  
Local Constraint ◽  
Liver Tumor Segmentation ◽  
Constraint Level ◽  
Level Set Model

Anisotropic Diffusion Combined with Nonconvex Functional for Noise Image Segmentation

2020 ◽  
pp. 2154009
Author(s):  
Ming Han ◽  
Jing-Qin Wang ◽  
Qian Dong ◽  
Jing-Tao Wang ◽  
Jun-Ying Meng
Keyword(s):  
Image Segmentation ◽  
Level Set ◽  
Anisotropic Diffusion ◽  
Functional Model ◽  
Solution Space ◽  
Energy Functional ◽  
Noise Image ◽  
Nonconvex Energy ◽  
Nonconvex Functional ◽  
Level Set Model

Aiming at the problems of low segmentation accuracy of noise image, poor noise immunity of the existing models and poor adaptability to complex noise environment, a noise image segmentation algorithm using anisotropic diffusion and nonconvex functional was proposed. First, focusing on the “staircase effect”, a nonconvex functional was introduced into the energy functional model for smooth denoising. Second, the validity and accuracy of the model were established by proving that there was no global minimum in the solution space of the nonconvex energy functional model; the improved model was then used to obtain a smooth and clear image edge while maintaining the edge integrity. Third, the smooth image obtained from the nonconvex energy functional model was combined with the level set model to obtain the anisotropic diffusion gray level set model. The optimal outline of the target was obtained by calculating the minimum value of the energy functional. Finally, an anisotropic diffusion equation with nonconvex energy functional model was built in this algorithm to segment noise image accurately and quickly. A series of comparative experiments on the proposed algorithm and similar algorithms were conducted. The results showed that the proposed algorithm had strong noise resistance and provided precise segmentation for noise image.

scholarly journals A Variational Level Set Model Combined with FCMS for Image Clustering Segmentation

2014 ◽  
Vol 2014 ◽  
pp. 1-24 ◽  
Author(s):  
Liming Tang
Keyword(s):  
Image Segmentation ◽  
Level Set ◽  
Clustering Algorithm ◽  
Energy Functional ◽  
Image Clustering ◽  
Clustering Model ◽  
Proposed Model ◽  
Fuzzy C Means Clustering ◽  
Variational Level Set ◽  
Level Set Model

The fuzzy C means clustering algorithm with spatial constraint (FCMS) is effective for image segmentation. However, it lacks essential smoothing constraints to the cluster boundaries and enough robustness to the noise. Samson et al. proposed a variational level set model for image clustering segmentation, which can get the smooth cluster boundaries and closed cluster regions due to the use of level set scheme. However it is very sensitive to the noise since it is actually a hard C means clustering model. In this paper, based on Samson’s work, we propose a new variational level set model combined with FCMS for image clustering segmentation. Compared with FCMS clustering, the proposed model can get smooth cluster boundaries and closed cluster regions due to the use of level set scheme. In addition, a block-based energy is incorporated into the energy functional, which enables the proposed model to be more robust to the noise than FCMS clustering and Samson’s model. Some experiments on the synthetic and real images are performed to assess the performance of the proposed model. Compared with some classical image segmentation models, the proposed model has a better performance for the images contaminated by different noise levels.

scholarly journals Contextual Level-Set Method for Breast Tumor Segmentation

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 189343-189353
Author(s):  
Sumaira Hussain ◽  
Xiaoming Xi ◽  
Inam Ullah ◽  
Yongjian Wu ◽  
Chunxiao Ren ◽  
...  
Keyword(s):  
Level Set Method ◽  
Level Set ◽  
Breast Tumor ◽  
Tumor Segmentation

Segmentation of Medical Image Based on Superpixel Boundary Perceptual Convolutional Network in Cancer Diagnosis and Treatment

2021 ◽  
Vol 11 (1) ◽  
pp. 254-260
Author(s):  
Xiaochun Yi ◽  
Jing Hou
Keyword(s):  
Breast Cancer ◽  
Cancer Diagnosis ◽  
Breast Tumor ◽  
Large Scale ◽  
Breast Cancer Diagnosis ◽  
Tumor Segmentation ◽  
Convolutional Network ◽  
Breast Segmentation ◽  
Individual Classifier

In order to reduce the computational complexity of breast tumor segmentation algorithms and improve the accuracy of breast segmentation, this paper proposes a breast tumor segmentation method based on super pixel boundary perceptual convolutional network. This method first uses super pixel segmentation convolutional network algorithm to segment breast medical images, and then uses region growth algorithm to achieve breast tumor segmentation at super pixel level. The research results show that in the classification of breast tumors, the fusion efficiency based on the classifier level is better than the fusion based on the feature set; the index R proposed and adopted in this paper can effectively select the appropriate individual classifier and generate a better performing integration 06%. Classifier, the accuracy of this classifier is 88.73%, the sensitivity is 97.06%. The method can be used to assist doctors in breast cancer diagnosis, improve the efficiency and accuracy of doctors' work diagnosis, and has certain significance for clinical research and large-scale screening of breast cancer.

Sign in / Sign up

Export Citation Format

Share Document