Application of imaging genetics method based on group sparse canonical correlation analysis in schizophrenia / 中国医学影像技术

Objective: To explore the correlation between imaging data and genetic data of schizophrenia patients using imaging genetics method. Methods A group sparse canonical analysis method was proposed, group sparse constraints λ1||u||G and λ2||v||G were added to sparse canonical correlation analysis model to select features groups. Then, features within one group were selected by sparse constraints τ1||u||1 and τ2||v||1. The imaging genetics method based on group sparse canonical correlation analysis method was used to analyze the correlation between brain regions and genes of schizophrenia, and the stability and ability of this method to select biomarkers were also verified. Results Several pairs canonical brain regions and genes were identified. The left insula and gene AKT1 demonstrated the most significant correlation (r=0.653 8), and r value between right rectus and gene DAOA, MAGI2 were larger than 0.6. The correlation coefficients of selected features were 0.626 9±0.016 1 with group sparse canonical correlation analysis and 0.625 5±0.018 1 with sparse canonical correlation analysis. After 10 selections, the proportion of 75 genes related to schizophrenia was higher than that of non-related genes randomly selected in the most related 20 genes selected by group sparse canonical correlation analysis. Conclusion: Several pairs canonical brain regions and genes can be identified by the group sparse canonical analysis method, which may provide a new way for the study of schizophrenia and other complex mental disorders.

Primary central nervous system lymphoma and glioblastoma image differentiation based on sparse representation system / 生物医学工程学杂志

It is of great clinical significance in the differential diagnosis of primary central nervous system lymphoma (PCNSL) and glioblastoma (GBM) because there are enormous differences between them in terms of therapeutic regimens. In this paper, we propose a system based on sparse representation for automatic classification of PCNSL and GBM. The proposed system distinguishes the two tumors by using of the different texture detail information of the two tumors on T1 contrast magnetic resonance imaging (MRI) images. First, inspired by the process of radiomics, we designed a dictionary learning and sparse representation-based method to extract texture information, and with this approach, the tumors with different volume and shape were transformed into 968 quantitative texture features. Next, aiming at the problem of the redundancy in the extracted features, feature selection based on iterative sparse representation was set up to select some key texture features with high stability and discrimination. Finally, the selected key features are used for differentiation based on sparse representation classification (SRC) method. By using ten-fold cross-validation method, the differentiation based on the proposed approach presents accuracy of 96.36%, sensitivity 96.30%, and specificity 96.43%. Experimental results show that our approach not only effectively distinguish the two tumors but also has strong robustness in practical application since it avoids the process of parameter extraction on advanced MRI images.

Face Image Retrieval of Efficient Sparse Code words and Multiple Attribute in Binning Image

ABSTRACT In photography, face recognition and face retrieval play an important role in many applications such as security, criminology and image forensics. Advancements in face recognition make easier for identity matching of an individual with attributes. Latest development in computer vision technologies enables us to extract facial attributes from the input image and provide similar image results. In this paper, we propose a novel LOP and sparse codewords method to provide similar matching results with respect to input query image. To improve accuracy in image results with input image and dynamic facial attributes, Local octal pattern algorithm [LOP] and Sparse codeword applied in offline and online. The offline and online procedures in face image binning techniques apply with sparse code. Experimental results with Pubfig dataset shows that the proposed LOP along with sparse codewords able to provide matching results with increased accuracy of 90%.

Novel Image Classification technique using Particle Filter Framework optimised by Multikernel Sparse Representation

ABSTRACT The robustness and speed of image classification is still a challenging task in satellite image processing. This paper introduces a novel image classification technique that uses the particle filter framework (PFF)-based optimisation technique for satellite image classification. The framework uses a template-matching algorithm, comprising fast marching algorithm (FMA) and level set method (LSM)-based segmentation which assists in creating the initial templates for comparison with other test images. The created templates are trained and used as inputs for the optimisation. The optimisation technique used in this proposed work is multikernel sparse representation (MKSR). The combined execution of FMA, LSM, PFF and MKSR approaches has resulted in a substantial reduction in processing time for various classes in a satellite image which is small when compared with Support Vector Machine (SVM) and Independent Component Discrimination Analysis (ICDA)based image classifications obtained for comparison purposes. This study aims to improve the robustness of image classification based on overall accuracy (OA) and kappa coefficient. The variation of OA with this technique, between different classes of a satellite image, is only10%, whereas that with the SVM and ICDA techniques is more than 50%.