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An Mean Shift Based Gray Level Co-occurrence Matrix for Endoscope Image Diagnosis

机译：基于均值漂移的灰度共生矩阵用于内窥镜图像诊断

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摘要

Endoscope is important for detecting gastric lesions. Computer aided analysis of endoscope images is helpful to improve the accuracy of endoscope tests. In this paper, Mean Shift-Gray Level Co-occurrence Matrix algorithm (MS-GLCM), an improved algorithm for computing Gray Level Co-occurrence Matrix (GLCM) based on Mean Shift, is presented to solve the problem that computing GLCM costs too much time. MS-GLCM is used in Color Wavelet Covariance(CWC) as a substitute for classical GLCM. The new CWC algorithm is applied to extract texture features, which are classified by AdaBoost, in endoscope images. Experiment shows that MS-GLCM saves the time cost and partly prevents from data redundancy, with a similar output like GLCM. And it decreases the final error rate in lesion detection of endoscope images.

机译：内窥镜对于检测胃部病变很重要。内窥镜图像的计算机辅助分析有助于提高内窥镜测试的准确性。本文提出了均值漂移-灰度共生矩阵算法（MS-GLCM），它是一种基于均值漂移的改进的灰度共生矩阵（GLCM）计算算法，解决了GLCM计算成本高的问题。很多时间。在彩色小波协方差（CWC）中使用MS-GLCM替代经典GLCM。新的CWC算法用于提取内窥镜图像中的纹理特征，这些纹理特征由AdaBoost分类。实验表明，MS-GLCM具有类似GLCM的输出，从而节省了时间成本并部分防止了数据冗余。并且降低了内窥镜图像病变检测中的最终错误率。

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来源
《Medical biometrics》|2010年|p.403-412|共10页
会议地点 Hong Kong(CN);Hong Kong(CN)
作者
Yilun Wu; Kai Sun; Xiaolin Lin; Shidan Cheng; Su Zhang;
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作者单位

Biomedical Engineering Dept., Shanghai Jiao Tong Univ., Shanghai, China;

Biomedical Engineering Dept., Shanghai Jiao Tong Univ., Shanghai, China;

Gastroenterology Dept., Ruijin Hospital, School of Medicine, Shanghai, China;

Gastroenterology Dept., Ruijin Hospital, School of Medicine, Shanghai, China;

Biomedical Engineering Dept., Shanghai Jiao Tong Univ., Shanghai, China;

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会议组织
原文格式 PDF
正文语种 eng
中图分类生物医学工程;
关键词
endoscope image; gray level co-occurrence matrix; mean shift; color wavelet covariance;

机译：内窥镜图像灰度共生矩阵;平均移动彩色小波协方差;

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An Mean Shift Based Gray Level Co-occurrence Matrix for Endoscope Image Diagnosis

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