[1]排孜丽耶·尤山塔依,严传波,木拉提·哈米提,等.肝包虫病和肝囊肿CT图像的分型研究[J].医学信息,2018,31(23):61-66.[doi:10.3969/j.issn.1006-1959.2018.23.018]
 Pazilya Yusantay,YAN Chuan-bo,Murat Hamit,et al.Classification of CT Images of Hepatic Echinococcosis and Hepatic Cyst[J].Journal of Medical Information,2018,31(23):61-66.[doi:10.3969/j.issn.1006-1959.2018.23.018]
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肝包虫病和肝囊肿CT图像的分型研究()
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医学信息[ISSN:1006-1959/CN:61-1278/R]

卷:
31卷
期数:
2018年23期
页码:
61-66
栏目:
论著
出版日期:
2018-12-01

文章信息/Info

Title:
Classification of CT Images of Hepatic Echinococcosis and Hepatic Cyst
文章编号:
1006-1959(2018)23-0061-06
作者:
排孜丽耶·尤山塔依1严传波2木拉提·哈米提2姚 娟3阿布都艾尼·库吐鲁克2
1.新疆医科大学基础医学院,新疆 乌鲁木齐 830011;2.新疆医科大学医学工程技术学院,新疆 乌鲁木齐 830011;3.新疆医科大学第一附属医院影像中心,新疆 乌鲁木齐 830011
Author(s):
Pazilya Yusantay1YAN Chuan-bo2Murat Hamit2YAO Juan3Abdugheni Kutluk2
1.College of Basic Medicine,Xinjiang Medical University,Urumqi 830011,Xinjiang,China;2.College of Medical Engineering Technology,Xinjiang Medical University,Urumqi 830011,Xinjiang,China;3.Image Center,the First Affiliated Hospital,Xinjiang Medical University,Urumqi 830011,Xinjiang,China
关键词:
肝包虫病肝囊肿图像融合特征提取图像分类
Keywords:
Hepatic echinococcosisHepatic cystImage fusionFeature extraction Image classification
分类号:
R532.32;R575.4
DOI:
10.3969/j.issn.1006-1959.2018.23.018
文献标志码:
A
摘要:
探讨计算机辅助诊断技术在肝包虫病和肝囊肿CT图像分型中的应用。方法 对单囊型肝包虫病和单发性肝囊肿CT图像感兴趣区域,分别使用传统的预处理方法和图像融合方法,提取原始ROI、预处理后的和融合后的ROI图像Haar小波、DB2小波、Tamura、Gabor滤波器和灰度-梯度共生矩阵特征,通过支持向量机和BP神经网络分类模型分类,比较三种方法的分类准确率,并对各分类模型进行参数评估。结果 从原始ROI图像直接提取的Haar小波、DB2小波、Tamura和GGCM特征的最佳分类准确率均达到了95%以上;融合后的ROI图像五种特征的分类准确率都较高,在90%以上。结论 本研究所使用的方法应用于肝包虫病和肝囊肿CT图像的分型中具有一定的分类优势,为影像学诊断提供依据。
Abstract:
Objective To discuss the application of computer aided diagnosis in classification of hepatic hydatid disease and hepatic cyst CT images.Methods For the region of the CT image of single cystic hepatic echinococcosis and single hepatic cyst, the original ROI, pre-processed and fused ROI image Haar wavelet, DB2 were extracted using traditional pre-processing methods and image fusion methods, respectively. Wavelet, Tamura, Gabor filter and gray-gradient co-occurrence matrix characteristics are classified by support vector machine and BP neural network classification model. The classification accuracy of the three methods is compared, and the parameters of each classification model are evaluated. Results The best classification accuracy of Haar wavelet, DB2 wavelet, Tamura and GGCM features extracted from the original ROI image reached more than 95%. The classification accuracy of the five characteristics of the ROI image after fusion is higher,above 90%.Conclusion The method used in this study has a certain classification advantage in the classification of CT images of hepatic hydatidosis and hepatic cysts, and provides a basis for imaging diagnosis.

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更新日期/Last Update: 2018-12-17