dc.creator | Chen, Zhanwen | |
dc.date.accessioned | 2020-08-23T15:47:25Z | |
dc.date.available | 2019-11-19 | |
dc.date.issued | 2019-11-19 | |
dc.identifier.uri | https://etd.library.vanderbilt.edu/etd-11182019-231129 | |
dc.identifier.uri | http://hdl.handle.net/1803/14615 | |
dc.description.abstract | Medical ultrasound is a noninvasive, affordable, portable, and real-time diagnostic modality that provides a cross-sectional view of tissues. Ultrasound beamforming is a widely used approach to acquire and process ultrasound data in a focused manner. However, noises from scatterers outside the main lobe of the beam degrade the beamformed images. Recently, frequency-domain multi-layer perceptrons (MLPs) prove effective in suppressing off-axis scattering and improving image contrast. This thesis extends the frequency-domain neural network approach to study the effectiveness of convolutional neural networks (CNNs). I propose a variety of convolutional architectures and investigate the contribution of the convolution operation. Preliminary results show that although hybrid convolutional- and full-connected neural networks can achieve a similar performance compared with MLPs, fully-convolutional neural networks do not perform well because they do not learn additional features. Instead, they approximate full connections by having a large effective receptive field. | |
dc.format.mimetype | application/pdf | |
dc.subject | neural networks | |
dc.subject | deep learning | |
dc.subject | ultrasound | |
dc.subject | beamforming | |
dc.subject | convolutional neural networks | |
dc.subject | convolution | |
dc.title | Noise Suppression in Ultrasound Beamforming Using Convolutional Neural Networks | |
dc.type | thesis | |
dc.contributor.committeeMember | Maithilee Kunda | |
dc.type.material | text | |
thesis.degree.name | MS | |
thesis.degree.level | thesis | |
thesis.degree.discipline | Computer Science | |
thesis.degree.grantor | Vanderbilt University | |
local.embargo.terms | 2019-11-19 | |
local.embargo.lift | 2019-11-19 | |
dc.contributor.committeeChair | Matthew Berger | |