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Volumetric texture features from higher-order images for diagnosis of colon lesions via CT colonography

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Abstract

Purpose

Differentiation of colon lesions according to underlying pathology, e.g., neoplastic and non-neoplastic lesions, is of fundamental importance for patient management. Image intensity-based textural features have been recognized as useful biomarker for the differentiation task. In this paper, we introduce texture features from higher-order images, i.e., gradient and curvature images, beyond the intensity image, for that task.

Methods

Based on the Haralick texture analysis method, we introduce a virtual pathological model 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 a database consisting of 148 colon lesions, of which 35 are non-neoplastic lesions, using the support vector machine classifier and the merit of area under the curve (AUC) of the receiver operating characteristics.

Results

The AUC of classification was improved from 0.74 (using the image intensity alone) to 0.85 (by also considering the gradient and curvature images) in differentiating the neoplastic lesions from non-neoplastic ones, e.g., hyperplastic polyps from tubular adenomas, tubulovillous adenomas and adenocarcinomas.

Conclusions

The experimental results demonstrated that texture features from 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 colonography for colorectal cancer screening by not only detecting polyps but also classifying them for optimal polyp management for the best outcome in personalized medicine.

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Acknowledgments

This work was partially supported by the NIH/NCI under Grant #CA082402 and #CA143111. H. Lu is partially supported by the National Natural Science Foundation of China under Grant #81230035 and #81071220 and also the National Key Technologies R&D Program of China under Grant #2011BAI12B03. P. Pickhardt was partially supported by the NIH/NCI under Grant #CA144835, #CA169331 and #CA155347. The authors would like to acknowledge the use of the Viatronix V3D-Colon Module.

Conflict of interest

Bowen Song, Guopeng Zhang, Hongbing Lu, Huafeng Wang, Wei Zhu, Perry J. Pickhardt and Zhengrong Liang declare that they have no conflict of interest. Dr. Liang is a co-founder of Viatronix. Dr. Pickhardt has served as a consultant for Viatronix, Braintree and Mindways and is co-founder of VirtuoCTC.

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Authors and Affiliations

  1. Department of Radiology, Stony Brook University, Stony Brook, NY , 11790, USA

    Bowen Song, Huafeng Wang & Zhengrong Liang

  2. Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY , 11790, USA

    Bowen Song & Wei Zhu

  3. Department of Biomedical Engineering, Fourth Military Medical University, Xi’an , 710032, Shaanxi, China

    Guopeng Zhang & Hongbing Lu

  4. Department of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, WI , 53792, USA

    Perry J. Pickhardt

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  1. Bowen Song
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  2. Guopeng Zhang
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Correspondence to Zhengrong Liang.

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Song, B., Zhang, G., Lu, H. et al. Volumetric texture features from higher-order images for diagnosis of colon lesions via CT colonography. Int J CARS 9, 1021–1031 (2014). https://doi.org/10.1007/s11548-014-0991-2

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  • DOI: https://doi.org/10.1007/s11548-014-0991-2

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