Volumetric texture features from higher-order images for diagnosis of colon lesions via CT colonography.
OBJECTIVE
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.
Department of Radiology, Stony Brook University, Stony Brook, NY , 11790, USA.|Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY , 11790, USA.
Bowen
B
Song
Department of Biomedical Engineering, Fourth Military Medical University, Xi'an , 710032, Shaanxi, China.
Guopeng
G
Zhang
Department of Biomedical Engineering, Fourth Military Medical University, Xi'an , 710032, Shaanxi, China.
Hongbing
H
Lu
Department of Radiology, Stony Brook University, Stony Brook, NY , 11790, USA.
Huafeng
H
Wang
Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY , 11790, USA.
Wei
W
Zhu
Department of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, WI , 53792, USA.
Perry
P
J Pickhardt
Department of Radiology, Stony Brook University, Stony Brook, NY , 11790, USA. jerome@mil.sunysb.edu.
Zhengrong
Z
Liang
D019540:Area Under Curve; D003110:Colonic Neoplasms / Q000000981:diagnostic imaging*; D023881:Colonography, Computed Tomographic / Q000379:methods; D006801:Humans; D007090:Image Interpretation, Computer-Assisted; D021621:Imaging, Three-Dimensional; D008875:Middle Aged; D012372:ROC Curve; D060388:Support Vector Machine
CT colonography; Colorectal lesions; Computer-aided diagnosis; Curvature; Gradient; Textural biomarker; Texture feature