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• W22967897 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." @default.
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• W22967897 date "2014-01-01" @default.
• W22967897 modified "2023-09-23" @default.
• W22967897 title "A Multi-Parametric Diffusion Magnetic Resonance Imaging Texture Feature Model for Prostate Cancer Analysis" @default.
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• W22967897 doi "https://doi.org/10.1007/978-3-319-11182-7_8" @default.
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