A5019133776- AI in cancer detection
- Ultrasound Imaging and Elastography
- Radiomics and Machine Learning in Medical Imaging
- Gene expression and cancer classification
- Advanced MRI Techniques and Applications
- Brain Tumor Detection and Classification
- Cerebrovascular and Carotid Artery Diseases
- Cardiovascular Health and Disease Prevention
- Medical Image Segmentation Techniques
- Photoacoustic and Ultrasonic Imaging
- Electrical and Bioimpedance Tomography
- MRI in cancer diagnosis
- Flow Measurement and Analysis
Zhengzhou University
2021-2024
Yunnan University
2017
A dynamic ultrasound simulation model for the common carotid artery (CCA) with three arterial layers validation of two-dimensional wall motion and blood velocity estimation algorithms is proposed in present study. This describes not only characteristics echo distributions conforming to clinical ones but also varying thicknesses, axial, radial displacements pulsatile pressure during a cardiac cycle.The modeling process as follows: first, geometrical according structure size CCA built based on...
Accurate breast detection and segmentation methods can improve the effectiveness of diagnosis disease, while simultaneously alleviating workload medical practitioners. In recent years, numerous have emerged for segmenting lesions. However, most them rely on B-mode ultrasound images exhibit limited understanding primary data. To accuracy segmentation, a algorithm based original RF signal is proposed in this paper. The first uses MimickNet technique noise reduction compression radio frequency...
Physicians typically combine multi-modal data to make a graded diagnosis of breast tumors. However, most existing tumor grading methods rely solely on image information, resulting in limited accuracy grading. This paper proposes Multi-information Selection Aggregation Graph Convolutional Networks (MSA-GCN) for Firstly, fully utilize phenotypic reflecting the clinical and pathological characteristics tumors, an automatic combination screening weight encoder is proposed data, which can...
Ultrasound harmonic imaging describes the nonlinear change caused by variation in tissue structure to expand scope of clinical diagnosis diseases and improve accuracy. Owing good adaptivity, accuracy performance for retaining second-harmonic component based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) algorithm outperforms those commonly used band-pass filtering (BPF) pulse inversion (PI) methods. However, immense computational load significantly hampers...
Owing to better-defined edges and enhanced contrast in multi-order harmonic imaging, the ultrasonic technique provides improved diagnosis performance for small lesions clinics. The separation based on filtering is a most frequently used method extracting harmonics from radio frequency (RF) echo signals. However, cutoff frequency, order, type of filter have great influence precision extraction. In present study, an adaptive empirical wavelet transform (EWT) algorithm proposed adaptively...