基于LBP和GLCM的肠道肿瘤图像特征提取方法  

Feature Extraction Method of Intestinal Tumor Images Based on LBP and GLCM

作　　者：杨波 张立娜[2] 韩霄松[3,4] YANG Bo;ZHANG Lina;HAN Xiaosong(School of Information Engineering,Changchun University of Finance and Economics,Changchun 130122,China;School of Information Technology,Jilin Agricultural University,Changchun 130118,China;Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education,Jilin University,Changchun 130012,China;College of Computer Science and Technology,Jilin University,Changchun 130012,China)

机构地区：[1]长春财经学院信息工程学院,长春130122 [2]吉林农业大学信息技术学院,长春130118 [3]吉林大学符号计算与知识工程教育部重点实验室,长春130012 [4]吉林大学计算机科学与技术学院,长春130012

出　　处：《吉林大学学报（理学版）》2022年第4期911-918,共8页Journal of Jilin University:Science Edition

基　　金：国家自然科学基金(批准号:61972174);吉林省科技发展计划项目(批准号:20210203211SF);吉林省教育厅科学技术研究项目(批准号:JJKH20201256KJ,JJKH20210335KJ).

摘　　要：针对肠道肿瘤图像样本有限导致肿瘤识别率低和收敛速度慢的问题,提出一种基于局部二值模式(LBP)和灰度共生矩阵(GLCM)的肠道肿瘤图像特征提取方法.首先,利用最大类间方差法自动计算图像灰度阈值,进行感兴趣区域的提取;然后,采用LBP+GLCM对肠道肿瘤部分图像进行特征提取,并利用支持向量机识别.对1500张肠道肿瘤图像进行实验的结果表明,该方法可达到94.84%的识别准确率,能有效辅助医学诊疗.Aiming at the problems of low tumor recognition rate and slow convergence speed caused by limited samples of intestinal tumor images,we proposed a feature extraction method of intestinal tumor images based on local binary pattern(LBP)and gray level co-occurrence matrix(GLCM).Firstly,the maximum interclass variance method was used to automatically calculate the image gray threshold and extract the region of interest.Secondly,the LBP+GLCM was used to extract the features of partial intestinal tumor image,and support vector machine was used for recognition.Experimental results of 1500 intestinal tumor images show that this method can achieve 94.84%recognition accuracy and can effectively assist medical diagnosis and treatment.

关 键 词：肠道肿瘤 感兴趣区域 局部二值模式 灰度共生矩阵 支持向量机 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]