北京生物医学工程

新疆哈萨克族食管癌图像特征提取及分型方法的探讨

Feature extraction and typing method of esophageal cancer images for Xinjiang Kazakh nationality

作者：茹仙古丽·艾尔西丁木拉提·哈米提严传波姚娟排孜丽耶·尤山塔依娜迪亚·阿卜杜迪克依木

单位：新疆医科大学基础医学院（乌鲁木齐830011）新疆医科大学医学工程技术学院（乌鲁木齐830011）新疆医科大学第一附属医院放射科（乌鲁木齐830054）

关键词：食管癌；灰度-梯度共生矩阵；灰度共生矩阵；特征提取；图像分类

分类号：R318.04

出版年·卷·期（页码）：2019·38·3（257-262）

摘要：

目的利用支持向量机（support vector machine ,SVM）对新疆哈萨克族X线食管造影图像进行特征提取及分型研究,为食管癌影像学诊断提供参考。方法随机选取正常食管和蕈伞型食管癌X线造影图像各200幅，运用灰度-梯度共生矩阵法和灰度共生矩阵法提取图像的特征，在SVM类型设置上选择C-SVC，选择多项式核函数，通过调整C-SVC分类器的参数进行实验。结果共计提取23维特征，利用单一特征算法进行分类，灰度-梯度共生矩阵法分类准确率为72.75％，灰度共生矩阵法分类准确率为85.25％，而混合纹理特征的分类准确率为86.25％。结论将纹理特征与SVM相结合对正常食管与蕈伞型食管癌X线造影图像进行特征提取及分析，具有较高的分类识别率，混合特征把图像纹理和灰度特征有效结合，提高了特征的分类能力，为食管癌的计算机辅助诊断系统的开发奠定了基础。

Objective To study the feature extraction and typing for X- ray images of Xinjiang Kazakh esophageal cancer based on support vector machine（SVM）.Methods We randomly selected 200 pieces normal esophagus X-ray image and mushroom esophageal carcinoma X-ray image. Gray gradient co-occurrence matrix and gray level co-occurrence matrix texture features were applied to extract the image features.And the feature classification ability was evaluated by C-SVC classifier.Conducting the experiment repeatedly by adjusting the C-SVC classifier. Results Twenty-three features were extracted by gray gradient co-occurrence matrix and gray level co-occurrence matrix.The experimental results showed that using single feature classification，the accuracy rate of gray gradient co-occurrence matrix and gray level co-occurrence matrix classification reached to 72.75％ and 85.25％,respectively.The accuracy rate of the comprehensive of gray gradient co-occurrence matrix and gray level co-occurrence matrix was 86.25%.It was more suitable for the classification of normal esophagus and mushroom esophageal carcinoma. Conclusions The characteristics of the normal esophagus and the mushroom esophageal carcinoma are extracted and analyzed with the SVM, which have high classification recognition rate，and laid the foundation for the development of computer-aided diagnosis system for esophageal cancer.

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