no code implementations • 16 Apr 2022 • Binjie Qin, Haohao Mao, Yiming Liu, Jun Zhao, Yisong Lv, Yueqi Zhu, Song Ding, Xu Chen
Although robust PCA has been increasingly adopted to extract vessels from X-ray coronary angiography (XCA) images, challenging problems such as inefficient vessel-sparsity modelling, noisy and dynamic background artefacts, and high computational cost still remain unsolved.
1 code implementation • 10 Feb 2021 • Dongdong Hao, Song Ding, Linwei Qiu, Yisong Lv, Baowei Fei, Yueqi Zhu, Binjie Qin
To efficiently discriminate vessel features from the complex and noisy backgrounds in the XCA images, the decoder stage effectively utilizes channel attention blocks to refine the intermediate feature maps from skip connection layers for subsequently decoding the refined features in 2D ways to produce the segmented vessel masks.
no code implementations • 26 Oct 2018 • Wenzhao Zhao, Qiegen Liu, Yisong Lv, Binjie Qin
For texture-preserving denoising of each cluster, considering that the variations in texture are captured and wrapped in not only the between-dimension energy variations but also the within-dimension variations of PCA transform coefficients, we further propose a PCA-transform-domain variation adaptive filtering method to preserve the local variations in textures.
no code implementations • 29 Mar 2013 • Binjie Qin, Zhijun Gu, Xianjun Sun, Yisong Lv
However, MI is sensitive to the "outlier" objects that appear in one image but not the other, and also suffers from local and biased maxima.
no code implementations • 3 Feb 2013 • Binjie Qin, Zhuangming Shen, Zien Zhou, Jiawei Zhou, Jiuai Sun, HUI ZHANG, Mingxing Hu, Yisong Lv
For nonrigid image registration, matching the particular structures (or the outliers) that have missing correspondence and/or local large deformations, can be more difficult than matching the common structures with small deformations in the two images.