Volumetric texture features from higher-order images for diagnosis of colon lesions via CT colonography.
Volumetric texture features from higher-order images for diagnosis of colon lesions via CT colonography.
复制标题
通过 CT 结肠成像诊断结肠病变的高阶图像的体积纹理特征
DOI：
10.1007/s11548-014-0991-2
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发表时间：
2014-11
影响因子：
3
通讯作者：
Liang, Zhengrong
中科院分区：
文献类型：
--
作者：
Song, Bowen;Zhang, Guopeng;Lu, Hongbing;Wang, Huafeng;Zhu, Wei;Pickhardt, Perry J.;Liang, Zhengrong
关键词：
根据基础病理(如肿瘤性和非肿瘤性病变)区分结肠病变对于患者管理至关重要。基于图像强度的纹理特征已被认为是区分任务的有用生物标志物。在本文中，我们引入了来自高阶图像的纹理特征，即梯度和曲率图像，而不是强度图像。在Haralick纹理分析方法的基础上，我们引入了一个虚拟病理模型，从图像强度分布的高阶微分，即梯度和曲率，来探索纹理特征的实用性。利用支持向量机分类器和曲线下面积(AUC)的优点，在包含148个结肠病变(其中35个是非肿瘤性病变)的数据库上验证了纹理特征。在区分增生性息肉与非肿瘤性病变方面，AUC从0.74(单独使用图像强度)提高到0.85(考虑梯度和曲率图像)，例如管状腺瘤、管状腺瘤和腺癌。实验结果表明，利用高阶图像的纹理特征可以显著提高大肠病变病理鉴别的分类准确率。分化能力的增强将增加计算机断层扫描结肠癌筛查的潜力，不仅检测息肉，还对它们进行分类，以便在个性化医疗中获得最佳的息肉治疗结果。
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. 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. 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. 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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影响因子：
10.6
作者：
Ji, Q;Engel, J;Craine, E
通讯作者：
Craine, E
影响因子：
158.5
作者：
Pickhardt, PJ;Choi, JR;Schindler, WR
通讯作者：
Schindler, WR
影响因子：
--
作者：
Fidler, JL;Johnson, CD;Harmsen, WS
通讯作者：
Harmsen, WS
DOI：
10.1109/tsmc.1973.4309314
发表时间：
1973-01-01
期刊：
IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS
影响因子：
--
作者：
HARALICK, RM;SHANMUGAM, K;DINSTEIN, I
通讯作者：
DINSTEIN, I
DOI：
10.1016/s1470-2045(13)70216-x
发表时间：
2013-07
期刊：
The Lancet. Oncology
影响因子：
--
作者：
Pickhardt PJ;Kim DH;Pooler BD;Hinshaw JL;Barlow D;Jensen D;Reichelderfer M;Cash BD
通讯作者：
Cash BD