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Mutual information (MI) is a popular similarity measure for image registration, whereby good registration can be achieved by maximizing the compactness of the clusters in the joint histogram. However, MI is sensitive to the ldquooutlierrdquo objects that appear in one image but not the other, and also suffers from local and biased maxima. We propose a novel(More)
X-ray spectral imaging provides quantitative imaging of trace elements in a biological sample with high sensitivity. We propose a novel algorithm to promote the signal-to-noise ratio (SNR) of x-ray spectral images that have low photon counts. Firstly, we estimate the image data area that belongs to the homogeneous parts through confidence interval testing.(More)
BACKGROUND Non-alcoholic fatty hepatitis (NASH) is highly prevalent, mitochondria damage is the main pathophysiological characteristic of NASH. However, treatment for mitochondria damage is rarely reported. METHODS NASH model was established in rats, the protective effects of curcumin were evaluated by histological observation; structure and function(More)
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. Most existing works depend heavily on the outlier segmentation to remove the outlier effect in the(More)
Objective: Numerous studies have shown that bone marrow-derived mesenchymal stem cells (MSCs) enhance neurological recovery after cerebral ischemia. However, the mechanisms are still not clear. The present study aimed to investigate the beneficial effects of MSCs on global cerebral ischemia induced by cardiac arrest (CA) and the underlying mechanisms.(More)
The problem addressed in this paper is matching corresponding regions in two images, even when the image has correspondence deficiency and local deformations. We present a novel algorithm for establishing region correspondences across images by graph matching and a novel local histogram based feature descriptor. Firstly, we segment the images into(More)
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