IIP Lab
Seminar

메뉴 건너뛰기

제 331차

2019.12.31 11:30

IIP연구실 조회 수:4045

발표내용 H-scan for visualizing types of tissue scatterers 
발표자 백지혜 
날짜 2020-01-03 

발표제목: H-scan for visualizing types of tissue scatterers

Abstract: The H-scan approach is a matched filter methodology that aims to add information to the traditional ultrasound B-scan.  The theory is based on the differences in the echoes produced by different classes of reflectors or scatterers.  Matched filters can be created for different types of scatterers, whereby the maximum output indicates a match and color schemes can be used to indicate the class of scatterer responsible for echoes, providing a visual interpretation of the results.  However, within the theory of weak scattering from a variety of shapes, small changes in the size of the inhomogeneous objects will create shifts in the scattering transfer function.  In this work, we determine the shift in center frequency, along with time and frequency domain characteristics of echoes and argue for a general power law transfer function to capture the major characteristics of scattering types.  With this general approach, the H-scan matched filters can be set to elicit more fine grain shifts in scattering types, commensurate with more subtle changes in tissue morphology.  Compensation for frequency dependent attenuation is helpful for avoiding beam softening effects with increasing depths.  Examples from phantoms and normal and pathological tissues are provided to demonstrate that the H-scan analysis and displays are sensitive to scatterer size and morphology, and can be adapted to conventional imaging systems.

System Group Seminar

회차 발표 내용 발표자 날짜
공지 2019 정보 및 시스템 그룹 연합 세미나    
제 333차 Estimation of sound speed maps from ultrasound & CT images for ultrasound beamforming 발표자 · 이재진  날짜 · 2020-02-07 
제 332차 Deep Arbitrary HDRI: Inverse Tone Mapping using Spatially-adaptive Normalization and Auxiliary Classifier 발표자 · 조소연  날짜 · 2020-01-17 
» H-scan for visualizing types of tissue scatterers 발표자 · 백지혜  날짜 · 2020-01-03 
제 330차 A real-time beamforming method based on complex Gaussian mixture model using frequency dependency for robust speech recognition 발표자 · 김정민  날짜 · 2019-12-27 
제 329차 패널 이미지의 규칙 영역 분리와 두 이미지 간 정합/오차 검출 발표자 · 박현근, 김동현  날짜 · 2019-10-18 
제 328차 Incorporating Geometric Knowledge into a Deep Neural Network 발표자 · 임성훈 (DGIST 정보통신융합전공 교수 )  날짜 · 2019-09-20 
제 327차 FUS interference elimination using singular value decomposition filtering for ultrasound guided FUS 발표자 · 정의석  날짜 · 2019-09-06 
제 326차 Removal and Synthesis: A Decomposition Method of Object Transfiguration 발표자 · 강건우  날짜 · 2019-08-16 
제 325차 Design and Implementation of a Wireless Carotid Neckband Doppler System with Wearable Ultrasound Sensors 발표자 · 송일섭  날짜 · 2019-08-02 
제 324차 Audio Event Detection Using Deep Learning for Road Surveillance 발표자 · 심샛별  날짜 · 2019-07-19 
제 323차 Nighttime Haze Removal Algorithm Based on Generative Adversarial Network 발표자 · 구범혁  날짜 · 2019-07-05 
제 322차 Deep audio-visual speech recognition 발표자 · 정준선  날짜 · 2019-06-07 
제 321차 Eigen-based adaptive clutter filter for Ultrasound spectral doppler 발표자 · 정지훈  날짜 · 2019-05-17 
제 320차 Knowledge distillation-based cascaded network compression in vehicle maker classification 발표자 · 이윤수  날짜 · 2019-05-03 
제 319차 Automatic range gate determination in PW spectral Doppler systems with a single element transducer 발표자 · 윤종민  날짜 · 2019-04-05 
제 318차 Beamforming methods for multi channel speech enhancement 발표자 · 조병준  날짜 · 2019-03-22 
제 317차 Single Image Nighttime Haze Removal Method Using Illumination Estimation 발표자 · 권선우  날짜 · 2019-03-08 
제 316차 Rectangular and Fermat’s Spiral Sparse Array Designs for 3D Medical Ultrasound Imaging 발표자 · 윤한솔  날짜 · 2019-02-15 
제 315차 Mixed Squeeze-and-Excitation Net for Single Image De-raining 발표자 · 안남현  날짜 · 2019-01-18 
제 314차 Ultrasound B-mode signal and image processing on multi-core SIMD CPU architecture 발표자 · 오선영  날짜 · 2019-01-04