Ultrasound-Based Characterization of Prostate Cancer Using Joint Independent Component Analysis

作者：Imani Farhad^*; Ramezani Mahdi; Nouranian Saman; Gibson Eli; Khojaste Amir; Gaed Mena; Moussa Madeleine; Gomez Jose A; Romagnoli Cesare; Leveridge Michael; Chang Silvia; Fenster Aaron; Siemens D Robert; Ward Aaron D; Mousavi Parvin; Abolmaesumi Purang

来源：IEEE Transactions on Biomedical Engineering, 2015, 62(7): 1796-1804.

DOI：10.1109/TBME.2015.2404300

• Joint independent component analysis (jICA)
• prostate cancer (PCa)
• RF time series

Objective: This paper presents the results of a new approach for selection of RF time series features based on joint independent component analysis for in vivo characterization of prostate cancer. Methods: We project three sets of RF time series features extracted from the spectrum, fractal dimension, and the wavelet transform of the ultrasound RF data on a space spanned by five joint independent components. Then, we demonstrate that the obtained mixing coefficients from a group of patients can be used to train a classifier, which can be applied to characterize cancerous regions of a test patient. Results: In a leave-one-patient-out cross validation, an area under receiver operating characteristic curve of 0.93 and classification accuracy of 84% are achieved. Conclusion: Ultrasound RF time series can be used to accurately characterize prostate cancer, in vivo without the need for exhaustive search in the feature space. Significance: We use joint independent component analysis for systematic fusion of multiple sets of RF time series features, within a machine learning framework, to characterize PCa in an in vivo study.

• 出版日期2015-7