Texture features based on high-order derivative maps for differentiation of bladder cancer / 医疗卫生装备
Chinese Medical Equipment Journal
;
(6): 12-16, 2017.
Artigo
em Chinês
| WPRIM
| ID: wpr-617199
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.
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Índice:
WPRIM (Pacífico Ocidental)
Idioma:
Chinês
Revista:
Chinese Medical Equipment Journal
Ano de publicação:
2017
Tipo de documento:
Artigo
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