Back to Search
Start Over
Ultrasound-Based Characterization of Prostate Cancer Using Joint Independent Component Analysis.
- Source :
-
IEEE Transactions on Biomedical Engineering . Jul2015, Vol. 62 Issue 7, p1796-1804. 9p. - Publication Year :
- 2015
-
Abstract
- 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. [ABSTRACT FROM PUBLISHER]
Details
- Language :
- English
- ISSN :
- 00189294
- Volume :
- 62
- Issue :
- 7
- Database :
- Academic Search Index
- Journal :
- IEEE Transactions on Biomedical Engineering
- Publication Type :
- Academic Journal
- Accession number :
- 103304515
- Full Text :
- https://doi.org/10.1109/TBME.2015.2404300