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Proceedings Paper

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

Paper Details

Date Published: 24 March 2014
PDF: 6 pages
Proc. SPIE 9035, Medical Imaging 2014: Computer-Aided Diagnosis, 90350C (24 March 2014); doi: 10.1117/12.2043916
Show Author Affiliations
Bowen Song, Stony Brook Medicine (United States)
Stony Brook Univ. (United States)
Guopeng Zhang, Fourth Military Medical Univ. (China)
Hongbing Lu, Fourth Military Medical Univ. (China)
Huafeng Wang, Stony Brook Medicine (United States)
Fangfang Han, Stony Brook Medicine (United States)
Wei Zhu, Stony Brook Univ. (United States)
Zhengrong Liang, Stony Brook Medicine (United States)

Published in SPIE Proceedings Vol. 9035:
Medical Imaging 2014: Computer-Aided Diagnosis
Stephen Aylward; Lubomir M. Hadjiiski, Editor(s)

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