The purpose of the study is to analyze whether certain components can be extracted in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) for the classification of prostate cancer (PCa). Nonnegative matrix factorization (NMF) was used to extract the characteristic curve from DCE-MRI. The peak sharpness of the characteristic curve was evaluated to classify prostates with and without PCa. Results showed that the peak sharpness of the characteristic curve was significantly different in prostates with and without PCa (p = 0.008) and the area under the receiver operating characteristic curve was 0.86 ± 0.08. We conclude that the NMF can decompose DCE-MRI into components and the peak sharpness of the characteristic curve has the promise to classify prostates with and without PCa accurately.
In order to improve the diagnostic effect of MRI images, a multiparametric magnetic resonance imaging (MRI) based classification method was proposed in this paper. The study included 85 patients. The radiomics method was used to extract morphological and texture features, while Apparent diffusion coefficient (ADC) was used as functional feature.Three classification methods, including Linear Discriminate Analysis (LDA), Support Vector Machine (SVM) and Random Forest (RF), were used to distinguish benign and malignant of pulmonary lesions. The performance of multiparametric MRI sequences and single sequences were compared. The experimental results shown that multiparametric MRI classification with SVM classifier had best performence (AUC=0.82±0.03), indicating that multiparametric MR diagnosis has great potential.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.