[1]娜迪亚·阿卜杜迪克依木,姚娟,刘志华,等.基于形状纹理特征的食管癌和肝包虫病图像分类[J].中国医学物理学杂志,2019,36(12):1427-1433.[doi:DOI:10.3969/j.issn.1005-202X.2019.12.012]
　NADIYA·Abdukeyim,YAO Juan,LIU Zhihua,et al.Image classification of esophageal cancer and hepatic hydatid disease based on shape and texture features[J].Chinese Journal of Medical Physics,2019,36(12):1427-1433.[doi:DOI:10.3969/j.issn.1005-202X.2019.12.012]
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基于形状纹理特征的食管癌和肝包虫病图像分类()
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《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]

卷:
36卷
期数:
2019年第12期
页码:
1427-1433
栏目:
医学影像物理
出版日期:
2019-12-25

文章信息/Info

Title:
Image classification of esophageal cancer and hepatic hydatid disease based on shape and texture features
文章编号:
1005-202X（2019）12-1427-07
作者:
娜迪亚·阿卜杜迪克依木1; 姚娟2; 刘志华3; 严传波4
1.新疆医科大学基础医学院， 新疆 乌鲁木齐 830011； 2.新疆医科大学第一附属医院， 新疆 乌鲁木齐 830011； 3.新疆医科大学公共卫生学院， 新疆 乌鲁木齐 830011； 4.新疆医科大学医学工程技术学院， 新疆 乌鲁木齐 830011
Author(s):
NADIYA·Abdukeyim1;  YAO Juan2;  LIU Zhihua3;  YAN Chuanbo4
1. Basic Medical College, Xinjiang Medical University, Urumqi 830011, China; 2. The First Affiliated Hospital of Xinjiang Medical University, Urumqi 830011, China; 3. College of Public Health, Xinjiang Medical University, Urumqi 830011, China; 4. Medical Engineering Technology College, Xinjiang Medical University, Urumqi 830011, China
关键词:
食管癌; 肝包虫; 医学图像; 特征提取; K最近邻
Keywords:
Keywords: esophageal cancer;  hepatic hydatid disease;  medical imaging;  feature extraction;  K nearest neighbor
分类号:
R318
DOI:
DOI:10.3969/j.issn.1005-202X.2019.12.012
文献标志码:
A
Abstract:
Abstract: Objective To explore the classification of Hu moment invariant features and wavelet transform texture features of the X-ray image of esophageal cancer and the CT image of hepatic hydatid disease by K nearest neighbor (KNN) classification algorithm. Methods Hu moment invariant and wavelet transform algorithms were used to extract the features of the X-ray image of esophageal cancer and the CT image of hepatic hydatid disease. Moreover, KNN classifier was used to classify the feature values for verifying the classification ability of the extracted features. Results For the X-ray image of esophageal cancer, Hu moment invariant algorithm had good classification performance in extracting shape features. Using wavelet transform algorithm to extract texture features of the CT image of hepatic hydatid disease also had preferable classification performance. Conclusion Hu moment invariant features combined with KNN classifiers provide a basis for the classification of esophageal cancer in Xinjiang Kazakh; and wavelet transform texture features combined with KNN classifiers provide a basis for the classification of endemic hepatic hydatid disease. The study also lays the foundation for the development of computer-aided diagnosis system.

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备注/Memo

备注/Memo:
【收稿日期】2019-07-05 【基金项目】国家自然科学基金（81560294） 【作者简介】娜迪亚·阿卜杜迪克依木，硕士研究生，研究方向：医学图像处理，E-mail: 86313302@qq.com 【通信作者】严传波，副教授，研究方向：生物信息处理、数据库应用，E-mail: ycbsky@126.com
更新日期/Last Update: 2019-12-26