摘要
针对14张脑瘤(Positron Emission Tomogrophy,PET)图像切片,将感兴趣区域分割出来,利用积分原理计算其体积特征.首先提取出图片所有点的像素值,然后利用自组织神经网络将像素值进行分类,其中自组织神经网络的输入节点经过多次的试验确定为2×2.通过自组织神经网络,可以将各个元素点进行标志,使得图片的分离简单精确并且计算出目标区域的元素点数.最后通过Matlab对14个切片构建出目标区域的三维立体图,利用积分原理算出该目标区域的体积为9.9 cm3.该方法合理,计算结果有效,且计算结果可对临床诊断有辅助作用.
A case study was conducted of the slicing of 14 cerebroma PET images, of which interesting parts were divided, with a calculation of their volume features followed. Firstly, all pixels in these images were extracted, and then classified according to the self-organizing neural network, the input node of which was determined as 2 ~ 2 through repeated experiments. By marking each element point through the self-organizing neural network, it is easier and more accurate to extract images and to calculate the num- ber of elements within the target zone. With the use of Matlab, it built the three-dimension chart of the target zone within the 14 slicings. Finally, the volume of target areas was calculated by using the integral principle. Experimental results show this theory is a plausible one with effective outcome, which can offer assistance in clinical diagnosis.
出处
《广东工业大学学报》
CAS
2013年第3期65-69,共5页
Journal of Guangdong University of Technology
基金
广东省大学生创新实验项目(1184510143)
关键词
自组织神经网络
正电子发射断层扫描
感兴趣区域
积分原理
图像分割
self organizing artificial neural network
positron emission tomography（PET）
region-of-in- terest
integral principle
image segmentation
