An mean shift based gray level co-occurrence matrix for endoscope image diagnosis

Authors:
Yilun Wu;Kai Sun;Xiaolin Lin;Shidan Cheng;Su Zhang
Affiliations:
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
Venue:
ICMB'10 Proceedings of the Second international conference on Medical Biometrics
Year:
2010

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Abstract

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.