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关于腰椎CT图像病灶区域优化检测仿真
Alternative TitleThe Simulation of the Focus Area Optimization of the Lumbar CT Image
李杨1,2; 梁炜1; 谈金东3
Department工业控制网络与系统研究室
Source Publication计算机仿真
ISSN1006-9348
2018
Volume35Issue:7Pages:327-330, 451
Contribution Rank1
Funding Organization国家自然科学基金重点项目(61333019)
Keyword腰椎图像 病灶区域 优化检测
Abstract对腰椎CT图像病灶区域的检测,能够有效提高腰椎疾病诊治效果。对腰椎CT图像病灶区域的检测,需要描述腰相CT图像的异质性势能函数,获取高斯混合模型聚类的输入样本值,完成腰椎CT图像病灶区域检测。传统方法计算图像特征设置不同的跟踪阈值,设置种群中每一个图像当前最优位置,但忽略了对图像的输入样本值的获取,导致检测精度偏低提出腰椎CT图像病灶区域检测方法。根据腰椎CT图像像素灰度空间关联性描述多个纹理特征,描述腰椎CT图像的同质区域之间的异质性势能函数,用不同子区域邻域关系势能函数来描述全局势能函数。提取病灶区域的区域块特征,并将其作为高斯混合模型聚类的输入样本值,通过聚类过程实现腰椎CT图像病灶区域检测。仿真证明,所提方法检测后的腰相CT图像病灶区域呈像清晰,局部细节信息损失度较小,图像不会发生过检测等现象。
Other AbstractA detection method for the lesion area of lumbar CT image was proposed. According to the gray spatial correlation of pixel in lumbar CT image, many texture features and the heterogeneous potential energy function between homogeneous regions of lumbar CT images were described. Then, the neighborhood potential energy functions from different sub-regions were used to describe global potential energy function. Moreover, the regional feature of lesions area was extracted, which was used as the input sample value of Gaussian mixture model clustering. Finally, the detection of lesion area of lumbar CT image was realized through clustering process. Simulation results show that the lesion area of lumbar CT image detected by proposed method has clear image. The loss degree of local detail information is small, and the image does not have the over detection.
Language中文
Document Type期刊论文
Identifierhttp://ir.sia.cn/handle/173321/22769
Collection工业控制网络与系统研究室
Corresponding Author李杨
Affiliation1.中国科学院沈阳自动化研究所
2.中国科学院大学
3.田纳西大学
Recommended Citation
GB/T 7714
李杨,梁炜,谈金东. 关于腰椎CT图像病灶区域优化检测仿真[J]. 计算机仿真,2018,35(7):327-330, 451.
APA 李杨,梁炜,&谈金东.(2018).关于腰椎CT图像病灶区域优化检测仿真.计算机仿真,35(7),327-330, 451.
MLA 李杨,et al."关于腰椎CT图像病灶区域优化检测仿真".计算机仿真 35.7(2018):327-330, 451.
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