摘要
在肝脏肿瘤疾病计算机辅助检测及诊断过程中,CT图像肝脏肿瘤分割属于是重要环节,因此在临床中CT图像肝脏肿瘤分割具有重要研究意义。传统几何形变模型在对比度较高的图像中应用更为适合,但是CT图像肝脏肿瘤通常情况下灰度不均匀、对比度偏低,没有良好的分割效果。基于这一问题,在对传统几何形变模型研究技术上,探讨CT图像肝脏肿瘤的新型形变模型分割方法。
In the process of computer-aided detection and diagnosis of liver tumors,the segmentation of liver tumors on CT images is an important part,so it is of great significance to study the segmentation of liver tumors on CT images in clinic.Traditional geometric deformation models are more suitable for high contrast images.However,in CT images of liver tumors,the gray scale is not uniform and the contrast is low,so there is no good segmentation effect.Based on this problem,a new segmentation method for liver tumors in CT images is discussed in terms of the traditional geometric deformation model technology.
作者
肖海慧
XIAO Hai-hui(Changzhou Vocational Institute of Textile and Garment,Changzhou Jiangsu 213164)
出处
《数字技术与应用》
2018年第10期90-93,共4页
Digital Technology & Application
基金
2017年度江苏省第五期"333工程"科研项目<基于深度卷积神经网络的肝脏肿瘤图像识别算法研究>(BRA2017484)课题研究成果
关键词
形变模型分割方法:CT图像
肝脏肿瘤
分割
deformable model segmentation method
CT image
liver tumor
segmentation