Abstract
In this work, we present a new multi-parametric magnetic resonance imaging (MP-MRI) texture feature model for automatic detection of prostate cancer. In addition to commonly used imaging sequences in conventional MP-MRI, namely T2-weighted MRI (T2w) and diffusion-weighted imaging (DWI), our proposed MP-MRI texture feature model uses computed high-b DWI (CHB-DWI) and a new diffusion imaging sequence called correlated diffusion imaging (CDI). A set of texture features was calculated for both the conventional MP-MRI and new MP-MRI texture feature model. We evaluated the performance of the proposed MP-MRI texture feature model via leave-one-patient-out cross-validation using a Bayesian classifier trained on cancerous and healthy tissue samples obtained from real clinical MP-MRI datasets. The proposed MP-MRI texture feature model outperformed the conventional model (i.e., T2w+DWI) with regard to cancer detection accuracy.
Keywords
- Prostate Cancer
- Apparent Diffusion Coefficient
- Clinical Decision Support System
- Bayesian Classifier
- Prostate Cancer Detection
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Khalvati, F., Modhafar, A., Cameron, A., Wong, A., Haider, M.A. (2014). A Multi-Parametric Diffusion Magnetic Resonance Imaging Texture Feature Model for Prostate Cancer Analysis. In: O'Donnell, L., Nedjati-Gilani, G., Rathi, Y., Reisert, M., Schneider, T. (eds) Computational Diffusion MRI. Mathematics and Visualization. Springer, Cham. https://doi.org/10.1007/978-3-319-11182-7_8
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DOI: https://doi.org/10.1007/978-3-319-11182-7_8
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