The nature of statistical learning theory
The nature of statistical learning theory
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Working Set Selection Using Second Order Information for Training Support Vector Machines
The Journal of Machine Learning Research
Tissue characterization using fractal dimension of high frequency ultrasound RF time series
MICCAI'07 Proceedings of the 10th international conference on Medical image computing and computer-assisted intervention
Hi-index | 0.00 |
We describe a very efficient method based on ultrasound RF time series analysis and support vector machine classification for generating probabilistic prostate cancer colormaps to augment the biopsy process. To form the RF time series, we continuously record ultrasound RF echoes backscattered from tissue while the imaging probe and the tissue are stationary in position. In an in-vitrostudy involving 30 prostate specimens, we show that the features extracted from RF time series are significantly more accurate and sensitive compared to two other established categories of ultrasound-based tissue typing methods. The method results in an area under ROC curve of 0.95 in 10-fold cross-validation.