基于快速三维主成分分析的肺CT图像检测

王青竹; 王珂; 李勇; 王新竹; 王斌

doi:10.3788/OPE.20101812.2695

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基于快速三维主成分分析的肺CT图像检测

信息科学 | 更新时间：2020-08-12

- 基于快速三维主成分分析的肺CT图像检测
- Detection of pulmonary CT scanned images based on fast 3D PCA
- 光学精密工程 2010年18卷第12期页码：2695-2701
- 作者机构：
  
  1. 吉林大学通信工程学院, 吉林长春 130025
  2. 吉林工程技术师范学院信息工程学院,吉林长春 130052
  3. 吉林省肿瘤医院, 吉林长春 130012
- 作者简介：
- 基金信息：
  
  国家863高技术研究发展计划资助项目(No.2007AA12Z242)()
- DOI：10.3788/OPE.20101812.2695
  中图分类号： R814.42;TP391.4
- 收稿日期：2010-04-06，
  
  修回日期：2010-05-18，
  
  网络出版日期：2010-12-25，
  
  纸质出版日期：2010-12-25
- 稿件说明：
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王青竹, 王珂, 李勇, 王新竹, 王斌. 基于快速三维主成分分析的肺CT图像检测[J]. 光学精密工程, 2010,18(12): 2695-2701

WANG Qing-zhu, WANG Ke, LI Yong, WANG Xin-zhu, WANG Bin. Detection of pulmonary CT scanned images based on fast 3D PCA[J]. Editorial Office of Optics and Precision Engineering, 2010,18(12): 2695-2701
王青竹, 王珂, 李勇, 王新竹, 王斌. 基于快速三维主成分分析的肺CT图像检测[J]. 光学精密工程, 2010,18(12): 2695-2701 DOI： 10.3788/OPE.20101812.2695.

WANG Qing-zhu, WANG Ke, LI Yong, WANG Xin-zhu, WANG Bin. Detection of pulmonary CT scanned images based on fast 3D PCA[J]. Editorial Office of Optics and Precision Engineering, 2010,18(12): 2695-2701 DOI： 10.3788/OPE.20101812.2695.

摘要

为解决基于二维图像处理的计算机辅助诊疗系统(CAD)仅考虑每幅图像自身包含的信息而忽略不同扫描层之间的联系

以及数据处理过程中的海量计算问题

提出一种新的基于快速三维主成分分析(3D PCA)的有效肺CT病灶检测算法。该算法首先引入高维张量奇异值分解(HOSVD)设计3D PCA;然后以提取出的三维空间特征点为种子点

进行区域增长以获取完整的疑似病灶区域;最后

根据医学图像具体特征

设计了一种HOSVD的简化分解算法。对来自吉林省肿瘤医院的10个典型病例的五百余幅临床CT图像进行了实验

并将实验结果与当前同类算法做了比较。结果表明

检测精确度提高了约10%~21%;另外

快速算法与原算法比较

计算复杂度可降低约1/3。由于快速3D PCA可以挖掘更多存在于不同连续扫描层间的有用信息

更精准提取病灶特征

在一定程度上提高了检测率。

Abstract

To solve the problem that the current Computer Aided Diagnosis(CAD) based 2D schemes only processes the formation for each scanned image itself and ignors the relation between the images

moreover

to reduce the computational complexity of high-order technology in an actual project

a more effective algorithm to detect pulmonary lesions in CT scanned images based on the fast Three Dimension Principle Component Analysis (3DPCA) was presented. Firstly

the Higher-Order Singular Value Decomposition (HOSVD) was introduced to design the 3D PCA. Then

by choosing the feature points as the seed points

the region grow was used to obtain the whole suspected lesion. Finally

a fast decomposition algorithm was presented according to the feature of CT scanned images. The technique was tested against more than 500 CT images form 10 typical cases of Jilin Tumor Hospital. The results confirm the validity of technique as well as enhanced performance. Comparing with other algorithms

the ratio of detection is improved by 10%-21%

and the computation is reduced to 1/3 of the original algorithm. Obtained data show that the fast 3D PCA could excavate more information exiting in the successive CT images. Moreover

improved high order technology can be more effectively applied in actual projects.

关键词

Keywords

references

JAFFAR M A, HUSSAIN A, MIRZA A M. Fuzzy entropy based optimization of clusters for the segmentation of lungs in CT scanned images [J]. Knowl Inf Syst., 2009,7:1-21.[2] ZHU Y, TAN Y Q, HUA Y Q, et al.. Feature selection and performance evaluation of support vector machine-based classifier for differentiating benign and malignant pulmonary nodules by computed tomography [J]. Journal of Digital Imaging, 2010,23(1):51-65.[3] ARMATO S G, GIGER M L, MORAN C J. Computerized detection of pulmonary nodules on CT scans[J]. Radio Graphics ,1999,19:1303-1311.[4] ANTONELLI M, LAZZERINI B, MARCELLONI F. Segmentation and reconstruction of the lung volume in CT image . 20th annual ACM symposium on applied computing, vol I. Santa Fe, New Mexico, 2000,5:255-299.[5] ZHAO B, GAMSU G, GINSBERG M S. Automatic detection of small lung nodules on CT utilizing a local density maximum algorithm [J].Journal of Applied Clinical Medical Physics,2003,4(3):248-259.[6] DEHMESHKI J, YE X, VALDIVIESO M. Automated detection of lung nodules in CT images using shape-based genetic algorithm [J]. Comput Med Imaging Graph. 2007,31(6):408-417.[7] REBELO M S, FURUIE S S, GUTIERREZ M A, et al.. Multiscale representation for automatic identification of structures in medical images [J]. Comput Biol Med., 2007,37(8):1183-1193.[8] YANG J, ZHANG D, FRANGI A F, et al.. Two-dimensional PCA: a new approach to appearance based face representation and recognition . IEEE Trans PAMI, 2004,26(1):131-137.[9] YANG J, YANG J Y. From image vector to matrix: a straightforward image projection technique-IMPCA vs. PCA [J]. Pattern Recognition.2002,35(9):1997-1999.[10] LIEVEN D L, BART D M, JOOS V. A multilinear singular value decomposition [J]. Siam J. Matrix Anal. Appl., 2000,21(4):1253-1278.[11] ROLAND B, REMY B. Multilinear singular value decomposition for structured tensors . IEEE ICASSP Conference,2006:1-14.[12] JUSSI S, ANDREAS R, VISA K J S. Sequential unfolding SVD for tensors with applications in array signal processing . IEEE Transactions on Ransactions on Signal Processing,2009,57(12):4719-4733.

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