Texture features based on high-order derivative maps for differentiation of bladder cancer
10.7687/j.issn1003-8868.2017.06.012
- VernacularTitle:基于高阶导数变换的纹理特征在膀胱肿瘤中的应用研究
- Author:
Xiaopan XU
;
Xuehan CAO
;
Juanli YUAN
;
Hongbing LU
;
Bowei CAO
- Keywords:
bladder cancer;
MRI;
three-dimensional texture feature;
high-order partial derivative transformation;
texture feature extraction
- From:
Chinese Medical Equipment Journal
2017;38(6):12-16
- CountryChina
- Language:Chinese
-
Abstract:
Objective To determine the three-dimensional (3D) texture features extracted from intensity and high-order derivative maps that could reflect textural differences between bladder tumors and wall tissues,in order to achieve bladder cancer and wall tissue identification.Methods A total of 62 cancerous and 62 wall volumes of interest (VOI) were extracted from T2-weighted MRI datasets of 62 patients with pathologically confirmed bladder cancer.To reflect heterogeneous distribution of tumor tissues,3D high-order derivative maps (the gradient and curvature maps) were calculated from each VOI.Then 3D Haralick features based on intensity and high-order derivative maps and Tamura features based on intensity maps were extracted from each VOI.Statistical analysis was proposed to first select the features with significant differences and then obtain a more predictive and compact feature subset to verify its differentiation performance.Results From each VOI,a total of 58 texture features were derived.Among them,37 features showed significant inter-class differences (P≤ 0.01).Conclusion The results suggest that 3D texture features deriving from intensity and high-order derivative maps can reflect heterogeneous distribution of cancerous tissues.
