Pseudo-Marginal MCMC Sampling for Image Segmentation Using Nonparametric Shape Priors
Sign Up to like & getrecommendations! Published in 2019 at "IEEE Transactions on Image Processing"
DOI: 10.1109/tip.2019.2922071
Abstract: Segmenting images of low quality or with missing data is a challenging problem. In such scenarios, exploiting statistical prior information about the shapes to be segmented can improve the segmentation results significantly. Incorporating prior density… read more here.
Keywords: shape; shape priors; density; pseudo marginal ... See more keywords
Robust Focus Volume Regularization in Shape From Focus
Sign Up to like & getrecommendations! Published in 2021 at "IEEE Transactions on Image Processing"
DOI: 10.1109/tip.2021.3100268
Abstract: Shape from focus (SFF) reconstructs 3D shape of the scene from a sequence of multi-focus images, and the quality of reconstructed shape mainly depends on the accuracy of image focus volume (FV). Traditional SFF techniques… read more here.
Keywords: focus volume; shape; shape priors; focus ... See more keywords
A new method incorporating deep learning with shape priors for left ventricular segmentation in myocardial perfusion SPECT images
Sign Up to like & getrecommendations! Published in 2022 at "Computers in biology and medicine"
DOI: 10.48550/arxiv.2206.03603
Abstract: Accurate segmentation of the left ventricle (LV) is crucial for evaluating myocardial perfusion SPECT (MPS) and assessing LV functions. In this study, a novel method combining deep learning with shape priors was developed and validated… read more here.
Keywords: perfusion spect; segmentation; myocardial perfusion; method ... See more keywords