Localization of Bone Surfaces from Ultrasound Data Using Local Phase Information and Signal Transmission Maps

2018 ◽  
pp. 1-11
Author(s):  
Ilker Hacihaliloglu
Keyword(s):  
Signal Transmission ◽  
Local Phase ◽  
Phase Information

Utilizing Local Phase Information to Remove Rain from Video

2014 ◽  
Vol 112 (1) ◽  
pp. 71-89 ◽  
Author(s):  
Varun Santhaseelan ◽  
Vijayan K. Asari
Keyword(s):  
Local Phase ◽  
Phase Information

Non-Rigid Ultrasound Image Registration Based on Intensity and Local Phase Information

2008 ◽  
Vol 54 (1-3) ◽  
pp. 33-43 ◽  
Author(s):  
Jonghye Woo ◽  
Byung-Woo Hong ◽  
Chang-Hong Hu ◽  
K. Kirk Shung ◽  
C.-C. Jay Kuo ◽  
...  
Keyword(s):  
Image Registration ◽  
Ultrasound Image ◽  
Local Phase ◽  
Phase Information

Zero Phase Wavelet Transform for Detecting Bearing Localized Defects

2005 ◽  
Author(s):  
C. James Li
Keyword(s):  
Wavelet Transform ◽  
Defect Detection ◽  
Diagnostic Information ◽  
Local Phase ◽  
Phase Information ◽  
Frequency Domains ◽  
Localized Defects ◽  
Envelope Spectrum ◽  
Zero Phase ◽  
Additional Phase

This study designed a wavelet in such a way that its Fourier transform has zero phase (hence the name zero-phase wavelet). Since the wavelet has zero phase, the resulted wavelet transform’s phase part solely depends on the phase of the signal within the passing band of the wavelet used. With its ability to localize in time and frequency domains, the magnitude part reveals local amplitude modulation and the phase part reveals local phase modulations associated with the frequency band defined by the wavelet. This study established the utility of the zero phase wavelet for bearing localized defect detection, examined the magnitude and phase of zero phase wavelet transform of bearing signals to determine if they can detect periodic structural ringings as effectively as the prevailing envelope spectrum technique and if the phase part brings any additional diagnostic information. It was found that the zero-phase wavelet transform is more effective than the envelope spectrum and the additional phase information complements the amplitude information and enhances the sensitivity for the detection.

scholarly journals Automatic Scan Plane Identification from 2D Ultrasound for Pedicle Screw Guidance

10.29007/nkvg ◽  
2018 ◽  
Author(s):  
Xiao Qi ◽  
Nilay Vora ◽  
Luis Riera ◽  
Amrut Sarangi ◽  
George Youssef ◽  
...  
Keyword(s):  
Imaging Modality ◽  
Signal Transmission ◽  
Orthopedic Procedures ◽  
Bone Surface ◽  
Phase Information ◽  
L1 Norm ◽  
Identification Rate ◽  
Surface Localization ◽  
2D Ultrasound ◽  
Localization Approach

In order to reduce the total amount of radiation exposure and provide real-time guidance ultrasound has been incorporated as a potential intra-operative imaging modality into various orthopedic procedures. However, high levels of noise, various imaging artifacts, and bone boundaries appearing several millimeters in thickness hinder the success of ultrasound as an alternative imaging modality in assisting orthopedic surgery procedures. Additional difficulties are also encountered during manual operation of the ultrasound transducer during image acquisition. In this work, we proposed a combination of novel scan plane identification method, based on convolutional neural networks, and bone surface localization method. The bone surface localization approach utilizes both local phase information, a combination of three different local image phase information and signal transmission map obtained from an L1 norm based contextual regularization method. The proposed network was utilized on two different US systems and to identify five different scan planes. Validation was performed on scans obtained from 16 volunteers. The correct scan plane identification rate of over 93% has been obtained. Validation against expert segmentation achieved a mean vertebra surface localization error of 0.42 mm.

scholarly journals A Motion Detection Algorithm Using Local Phase Information

2016 ◽  
Vol 2016 ◽  
pp. 1-20 ◽  
Author(s):  
Aurel A. Lazar ◽  
Nikul H. Ukani ◽  
Yiyin Zhou
Keyword(s):  
Motion Detection ◽  
Building Block ◽  
Moving Objects ◽  
Reconstruction Algorithm ◽  
Building Blocks ◽  
Detection Algorithm ◽  
Local Phase ◽  
Local Motion ◽  
Phase Information ◽  
Temporal Derivative

Previous research demonstrated thatglobalphase alone can be used to faithfully represent visual scenes. Here we provide a reconstruction algorithm by using onlylocalphase information. We also demonstrate that local phase alone can be effectively used to detect local motion. The local phase-based motion detector is akin to models employed to detect motion in biological vision, for example, the Reichardt detector. The local phase-based motion detection algorithm introduced here consists of two building blocks. The first building block measures/evaluates the temporal change of the local phase. The temporal derivative of the local phase is shown to exhibit the structure of a second order Volterra kernel with two normalized inputs. We provide an efficient, FFT-based algorithm for implementing the change of the local phase. The second processing building block implements the detector; it compares the maximum of the Radon transform of the local phase derivative with a chosen threshold. We demonstrate examples of applying the local phase-based motion detection algorithm on several video sequences. We also show how the locally detected motion can be used for segmenting moving objects in video scenes and compare our local phase-based algorithm to segmentation achieved with a widely used optic flow algorithm.

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