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Proceedings Paper

Differentiating bladder carcinoma from bladder wall using 3D textural features: an initial study
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Paper Abstract

Differentiating bladder tumors from wall tissues is of critical importance for the detection of invasion depth and cancer staging. The textural features embedded in bladder images have demonstrated their potentials in carcinomas detection and classification. The purpose of this study was to investigate the feasibility of differentiating bladder carcinoma from bladder wall using three-dimensional (3D) textural features extracted from MR bladder images. The widely used 2D Tamura features were firstly wholly extended to 3D, and then different types of 3D textural features including 3D features derived from gray level co-occurrence matrices (GLCM) and grey level-gradient co-occurrence matrix (GLGCM), as well as 3D Tamura features, were extracted from 23 volumes of interest (VOIs) of bladder tumors and 23 VOIs of patients’ bladder wall. Statistical results show that 30 out of 47 features are significantly different between cancer tissues and wall tissues. Using these features with significant differences between these two types of tissues, classification performance with a supported vector machine (SVM) classifier demonstrates that the combination of three types of selected 3D features outperform that of using only one type of features. All the observations demonstrate that significant textural differences exist between carcinomatous tissues and bladder wall, and 3D textural analysis may be an effective way for noninvasive staging of bladder cancer.

Paper Details

Date Published: 24 March 2016
PDF: 9 pages
Proc. SPIE 9785, Medical Imaging 2016: Computer-Aided Diagnosis, 97851T (24 March 2016); doi: 10.1117/12.2216821
Show Author Affiliations
Xiaopan Xu, Fourth Military Medical Univ. (China)
Xi Zhang, Fourth Military Medical Univ. (China)
Yang Liu, Fourth Military Medical Univ. (China)
Qiang Tian, Tangdu Hospital (China)
Guopeng Zhang, Fourth Military Medical Univ. (China)
Hongbing Lu, Fourth Military Medical Univ. (China)

Published in SPIE Proceedings Vol. 9785:
Medical Imaging 2016: Computer-Aided Diagnosis
Georgia D. Tourassi; Samuel G. Armato III, Editor(s)

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