SIA OpenIR  > 工业控制网络与系统研究室
Alternative TitleTexture Optimization Simulation of the Potential Lesion Area of the Patient's Lung Image
李杨1,2; 梁炜1; 谈金东3
Source Publication计算机仿真
Contribution Rank1
Funding Organization国家自然科学基金重点项目(61333019)
Keyword肺部图像 潜在病变区域 图像纹理识别
Other AbstractTraditional methods ignore the definition of texture energy function, which leads to low accuracy of texture recognition. In this article, we present a method to optimize and recognize the texture in potential lesion region of lung image based on level set segmentation. Firstly, we preprocessed the lung image in potential lesion region of patient and used the image gradient variance weighting information entropy algorithm to change filter parameters adaptively. Then, we used grayscale global information to initialize the level set. Meanwhile, we defined a local energy function by local grayscale fitting function of the image. Integrated with various texture features, we inputted fusion results to the Softmax layer of neural network to recognize the image texture in the potential lesion region. Simulation results prove that the proposed method has good accuracy and robustness.
Document Type期刊论文
Corresponding Author李杨
Recommended Citation
GB/T 7714
李杨,梁炜,谈金东. 患者肺部图像潜在病变区域纹理优化识别仿真[J]. 计算机仿真,2018,35(9):417-420.
APA 李杨,梁炜,&谈金东.(2018).患者肺部图像潜在病变区域纹理优化识别仿真.计算机仿真,35(9),417-420.
MLA 李杨,et al."患者肺部图像潜在病变区域纹理优化识别仿真".计算机仿真 35.9(2018):417-420.
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