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Compressed sensing (CS) theory has attracted much attention in the field of signal and image processing. In this paper, the ideas and methods of the image super-resolution (SR) reconstruction combined with CS is studied. The challenge is how to reconstruct a SR image when only one low- resolution (LR) image is available. Regularization methods are the important image SR techniques. Recently, the transform-invariant...
The expectation-maximization (EM) algorithm has been widely applied to the estimation of Gaussian mixture model (GMM) in brain MR image segmentation. However, the EM algorithm is deterministic and intrinsically prone to overfitting the training data and being trapped in local optima. In this paper, we propose a hybrid genetic and variational EM (GA-VEM) algorithm for brain MR image segmentation. In...
The accurate segmentation of PET-only brain images is challenging because of the low spatial resolution and high noise level in PET data. PET/CT has now replaced PET and offers the opportunity to improve segmentation through the high resolution, lower noise CT data. This paper pioneers the research of PET-CT brain image segmentation, which takes advantage of the full information available from the...
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