Ultrasound-based Characterization of Prostate Cancer using Joint Independent Component Analysis
IEEE Transactions on Biomedical Engineering(2015)
摘要
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-onepatient- 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.
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关键词
Joint independent component analysis (jICA), prostate cancer (PCa), RF time series
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