An improved high order texture features extraction method with application to pathological diagnosis of colon lesions for CT colonography

Bowen Song; Guopeng Zhang; Hongbing Lu; Huafeng Wang; Fangfang Han; Wei Zhu; Zhengrong Liang

doi:10.1117/12.2043916

24 March 2014 An improved high order texture features extraction method with application to pathological diagnosis of colon lesions for CT colonography

Bowen Song, Guopeng Zhang, Hongbing Lu, Huafeng Wang, Fangfang Han, Wei Zhu, Zhengrong Liang

Author Affiliations +

Proceedings Volume 9035, Medical Imaging 2014: Computer-Aided Diagnosis; 90350C (2014) https://doi.org/10.1117/12.2043916
Event: SPIE Medical Imaging, 2014, San Diego, California, United States

Abstract

Differentiation of colon lesions according to underlying pathology, e.g., neoplastic and non-neoplastic, is of fundamental importance for patient management. Image intensity based textural features have been recognized as a useful biomarker for the differentiation task. In this paper, we introduce high order texture features, beyond the intensity, such as gradient and curvature, for that task. Based on the Haralick texture analysis method, we introduce a virtual pathological method to explore the utility of texture features from high order differentiations, i.e., gradient and curvature, of the image intensity distribution. The texture features were validated on database consisting of 148 colon lesions, of which 35 are non-neoplastic lesions, using the random forest classifier and the merit of area under the curve (AUC) of the receiver operating characteristics. The results show that after applying the high order features, the AUC was improved from 0.8069 to 0.8544 in differentiating non-neoplastic lesion from neoplastic ones, e.g., hyperplastic polyps from tubular adenomas, tubulovillous adenomas and adenocarcinomas. The experimental results demonstrated that texture features from the higher order images can significantly improve the classification accuracy in pathological differentiation of colorectal lesions. The gain in differentiation capability shall increase the potential of computed tomography (CT) colonography for colorectal cancer screening by not only detecting polyps but also classifying them from optimal polyp management for the best outcome in personalized medicine.

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Bowen Song, Guopeng Zhang, Hongbing Lu, Huafeng Wang, Fangfang Han, Wei Zhu, and Zhengrong Liang "An improved high order texture features extraction method with application to pathological diagnosis of colon lesions for CT colonography", Proc. SPIE 9035, Medical Imaging 2014: Computer-Aided Diagnosis, 90350C (24 March 2014); https://doi.org/10.1117/12.2043916

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KEYWORDS

3D image processing

3D modeling

Colon

Feature extraction

Colorectal cancer

Computer aided diagnosis and therapy

Virtual colonoscopy

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