Value of fusion of MRI texture features in diagnosis of prostate cancer
10.13929/j.1003-3289.201810026
- Author:
Yongsen HAN
1
Author Information
1. School of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology
- Publication Type:Journal Article
- Keywords:
Adaptive threshold;
Local ternary pattern;
Magnetic resonance imaging;
Prostatic neoplasms;
Texture feature
- From:
Chinese Journal of Medical Imaging Technology
2019;35(5):769-773
- CountryChina
- Language:Chinese
-
Abstract:
Objective: To explore the value of three-dimensional optimization of threshold local ternary pattern (LTP) texture features, conventional texture features and grayscale statistical features fusion features for diagnosis of prostate cancer. Methods The peripheral zone of prostate was segmented from multi-sequence MR images. The optimization of the threshold LTP texture features, the conventional texture features and the grayscale statistical features was extracted. The fusion features were classified with Adaboost algorithm. The diagnostic efficacy was analyzed. Results: AUC of three-dimensional optimization of the threshold LTP fusion texture feature for predicting prostate cancer was 0.79±0.04, and the sensitivity, specificity and accuracy was 78.31% (65/83), 80.81% (80/99) and 79.67% (145/182), respectively. The AUC of conventional texture features for predicting prostate cancer was 0.71±0.04, and the sensitivity, specificity and accuracy was 72.29% (60/83), 81.82% (81/99), 77.47% (141/182), respectively. The AUC of grayscale statistical features for predicting prostate cancer was 0.80±0.04, and the sensitivity, specificity and accuracy was 78.31% (65/83), 82.83% (82/99), 80.77% (147/182), respectively. The AUC of fusion features for predicting prostate cancer was 0.87±0.04, and the sensitivity, specificity and accuracy was 86.75% (72/83), 88.89% (88/99) and 87.91% (160/182), respectively. Conclusion: The diagnostic efficacy of prostate cancer can be effectively improved by fusing local ternary patterns features, conventional texture features and grayscale statistical texture features.
