Machine learning for noise removal on breast ultrasound images
This study assessed the utility of machine learning for isolating noise and artifacts in breast ultrasound images. Such corrupt image regions (ROIs) can be automatically excluded when registering images acquired from different angles. Artifacts included posterior acoustic shadowing and enhancement arising from cancers and cysts respectively. Images were obtained on a breast-mimicking phantom containing multiple cysts and lesions with variable speed of sound and attenuation properties. In vivo breast images of cysts and cancers were also available. Results show that the classifiers were able to identify the regions of corrupt data accurately.
A new modeling method based on sample data is described in this paper,through physics and sample date analysis on displacement vs.voltage characteristics of x-y plane of cylindrical piezoelectric scanner used in scanning probe microscope(SPM).Through sample data statistical analysis,and considering the error factors such as scan speed,scan angle,nonlinearity and coupling,a model represented by a binomial plus some error expressions is constructed.A nonlinearity correction expression based on the model is deduced.With the software Matlab this nonlinearity correction is simulated.The result of simulation indicates that it can eliminate the errors effectively.The ascendency of the method is that there are no polarization mechanism analysis and fewer number of model parameters.The method is practical applied to engineering.