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International Journal of Computer Assisted Radiology and Surgery

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01-04-2017 | Original Article

Three-dimensional texture features from intensity and high-order derivative maps for the discrimination between bladder tumors and wall tissues via MRI

Authors: Xiaopan Xu, Xi Zhang, Qiang Tian, Guopeng Zhang, Yang Liu, Guangbin Cui, Jiang Meng, Yuxia Wu, Tianshuai Liu, Zengyue Yang, Hongbing Lu

Published in: International Journal of Computer Assisted Radiology and Surgery | Issue 4/2017

Abstract

Purpose

This study aims 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, and propose a noninvasive, image-based strategy for bladder tumor differentiation preoperatively.

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 better 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 and recursive feature elimination-based support vector machine classifier (RFE-SVM) 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 (\(P\le 0.01\)). With 29 optimal features selected by RFE-SVM, the classification results namely the sensitivity, specificity, accuracy and area under the curve (AUC) of the receiver operating characteristics were 0.9032, 0.8548, 0.8790 and 0.9045, respectively. By using synthetic minority oversampling technique to augment the sample number of each group to 200, the sensitivity, specificity, accuracy an AUC value of the feature selection-based classification were improved to 0.8967, 0.8780, 0.8874 and 0.9416, respectively.

Conclusions

Our results suggest that 3D texture features derived from intensity and high-order derivative maps can better reflect heterogeneous distribution of cancerous tissues. Texture features optimally selected together with sample augmentation could improve the performance on differentiating bladder carcinomas from wall tissues, suggesting a potential way for tumor noninvasive staging of bladder cancer preoperatively.

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Title: Three-dimensional texture features from intensity and high-order derivative maps for the discrimination between bladder tumors and wall tissues via MRI
Authors: Xiaopan Xu
Xi Zhang
Qiang Tian
Guopeng Zhang
Yang Liu
Guangbin Cui
Jiang Meng
Yuxia Wu
Tianshuai Liu
Zengyue Yang
Hongbing Lu
Publication date: 01-04-2017
Publisher: Springer International Publishing
Published in: International Journal of Computer Assisted Radiology and Surgery / Issue 4/2017
Print ISSN: 1861-6410
Electronic ISSN: 1861-6429
DOI: https://doi.org/10.1007/s11548-017-1522-8

Keynote webinar | Spotlight on sleep in brain health

Springer Medicine

Three-dimensional texture features from intensity and high-order derivative maps for the discrimination between bladder tumors and wall tissues via MRI

Abstract

Purpose

Methods

Results

Conclusions

Keynote webinar | Spotlight on sleep in brain health

Springer Medicine

Abstract

Purpose

Methods

Results

Conclusions

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