Chengcai Leng

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Optical molecular imaging is an important technique of studies at molecular level and provides promising tools to non-invasively delineate in vivo physiological and pathological activities at cellular and molecular levels, and it has been widely used for diagnosing, managing diseases, metastasis detection and drug development. From a mathematical(More)
This paper proposes an automatic registration technique based on steerable pyramid transform and robust shift invariant feature transform (SIFT) features, which can deal with the large variations of scale, rotation and illumination of the images. First, the steerable pyramid transform is used to the two input images and the sub-band images along certain(More)
Cerenkov luminescence imaging is an emerging optical technique for imaging the distribution of radiopharmaceuticals in vivo. However, because of the light scattering effect, it cannot obtain optical information from deep internal organs. To overcome this challenge, we established a novel endoscopic Cerenkov luminescence imaging system that used a clinically(More)
Optical molecular imaging is a promising technique and has been widely used in physiology, and pathology at cellular and molecular levels, which includes different modalities such as bioluminescence tomography, fluorescence molecular tomography and Cerenkov luminescence tomography. The inverse problem is ill-posed for the above modalities, which cause a(More)
This paper presents a method to construct efficient and distinctive descriptors for local image features based on Scale Invariant Features Transform (SIFT), namely, Kernel Independent Component Analysis Scale Invariant Features Transform (KICA-SIFT). KICA-SIFT is a improved version of the conventional SIFT for the two reasons: first, the improved SIFT(More)
Image registration is a key problem in a variety of applications, such as computer vision, medical image processing, pattern recognition, etc., while the application of registration is limited by time consumption and the accuracy in the case of large pose differences. Aimed at these two kinds of problems, we propose a fast rotation-free feature-based rigid(More)
This paper proposes a novel strength correspondence weighted graph for multi-scale image registration. First, the strength correspondence weighted graph is proposed based on the structure information and attributes relations of graph. Second, a similarity scale parameter obtained which can adjust adaptively scale based on the similarity of the different(More)
In this paper, structural vector autoregressive model is expressed as time series chain graphs. We show the equivalence between the edge of the chain graph and the corresponding coefficient of the structural VAR, and discuss the links between partial covariance among residuals and partial covariance among contemporaneous components in a VAR model. A(More)
Non-negative matrix factorization (NMF) is a good partsbased representation in computer vision. However, it fails to preserve or enhance the features and details of the data. To resolve this problem, we propose a novel sparse matrix factorization method for medical image registration, called Total Variation constrained Graph regularized Nonnegative Matrix(More)