A Calculation Method of the Volume of PET Image in the Region-of-interest

Wang Zhen-you; Zheng Shao-jie; He Yuan-cai; Lin Jie-zhi; Su Na; Long Jie-li

doi:10.3969/j.issn.1007-7162.2013.03.012

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A Calculation Method of the Volume of PET Image in the Region-of-interest

更新时间：2026-05-27

- A Calculation Method of the Volume of PET Image in the Region-of-interest
- Vol. 30, Issue 3, Pages: 65-69(2013)
- 作者机构：
- 作者简介：
- 基金信息：
- DOI：10.3969/j.issn.1007-7162.2013.03.012
  CLC：
- Online First：30 September 2013，
  
  Published：2013
- 稿件说明：
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王振友, 郑少杰, 何远才, 林洁芝, 苏娜, 龙洁丽. 一种PET图像感兴趣区域体积特征的计算方法[J]. 广东工业大学学报, 2013, 30(3): 65-69.

Wang Zhen-you, Zheng Shao-jie, He Yuan-cai, et al. A Calculation Method of the Volume of PET Image in the Region-of-interest[J]. 2013, 30(3): 65-69.
王振友, 郑少杰, 何远才, 林洁芝, 苏娜, 龙洁丽. 一种PET图像感兴趣区域体积特征的计算方法[J]. 广东工业大学学报, 2013, 30(3): 65-69. DOI： 10.3969/j.issn.1007-7162.2013.03.012.

Wang Zhen-you, Zheng Shao-jie, He Yuan-cai, et al. A Calculation Method of the Volume of PET Image in the Region-of-interest[J]. 2013, 30(3): 65-69. DOI： 10.3969/j.issn.1007-7162.2013.03.012.

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 cm

.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.

Abstract

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 number 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.

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