欢迎您！东篱公司
退出
-
申报数据库
-
立项数据库
-
成果数据库
-
项目获奖数据库
中文摘要：
有效地实现超声图像的分割依然是临床疾病诊断亟待解决的一个难题。本研究将图像树型框架小波变换、尺度共生矩阵、KL变换主分量分析和自组织神经网络聚类相结合应用于医学超声图像，提出一种分割新方法。实验表明，对于不同的医学超声图像，应用本研究方法均可得到比较清晰的分割结果，且显著地提高了分割图像的对比度，这对于固有对比度较低的医学超声图像来说不啻一种很有效的图像分割新方法，为临床诊断提供新的借鉴。
英文摘要：
It has been an urgent and tough problem to implement medical ultrasonic image segmentation effectively in clinical disease diagnosis. This paper proposed a new image segmentation method, which integrated the theory of tree- structured frame-wavelet transform, scale co-occurrence matrix （SCM）, principal component analysis and self- organizing neural network, and applied them to the clinical ultrasonic image finishing image segmentation. Experiment results showed that clearer segmented images with a high contrast were obtained with the proposed method.
同期刊论文项目
同项目期刊论文
Robust propagation velocity estimation of gastric electrical activity by least mean p-norm blind cha
期刊信息
位置：