Research On Anomaly Detection Algorithm In Electricity Market Based On Deep Generative Models

In recent years,with the continuous development of medical imaging technology,various modalities of imaging techniques such as CT and MR have been widely used in the diagnosis and treatment of cardiovascular diseases.However,the traditional analysis of structural features of cardiac tissues usually relies on manual examination and measurement by clinicians and researchers,which is very time-consuming and easily influenced by subjectivity.To address this issue,it is significant to define some new quantitative analysis methods for cardiac morphological structure to shift from visual observation to quantitative assessment,which can provide clinicians with a more intuitive diagnostic basis and has a more important practical application value.Additionally,due to limitations in imaging hardware and the objective presence of cardiac motion,cardiac medical images often contain noise and significant myocardial displacement,leading to a significant reduction in the accuracy of quantitative calculations.As a result,these calculations cannot be used for subsequent quantitative analysis of cardiac tissue structure,greatly affecting the diagnosis and treatment effectiveness of cardiovascular diseases by clinicians.To improve the accuracy of quantitative analysis,it is crucial to select appropriate post-processing methods based on the characteristics of cardiac tissue structure and image features and define their quantitative analysis models.This paper addresses the practical need for quantitative analysis of cardiac tissue structure in clinical practice.It focuses on the quantitative analysis and post-processing methods for the pectinate muscle of left atrial appendage based on cardiac CT images and the microstructure of the left ventricular myocardium based on cardiac magnetic resonance(MR)diffusion tensor imaging(DTI)images.The effectiveness of each quantitative analysis and post-processing method is validated in both healthy volunteers and patients.The main studies of this paper are as follows.Firstly,it introduces the imaging principles of CT and MR-DTI and common medical image processing methods.Gaussian filtering,median filtering,and anisotropic filtering are used to denoise cardiac CT images.The results show that anisotropic filtering can effectively remove noise while preserving the edge information of the left atrial appendage.The principles and applicable scenarios of three traditional image segmentation methods,namely thresholding,region growing,and active contour models,are analyzed.Based on the structural characteristics of the pectinate muscle,region growing is selected for left atrial appendage segmentation.The basic principles of image registration methods are explained,and the registration effects of three common linear transformations are compared.These provide a theoretical foundation and reference value for subsequent research on quantitative analysis and post-processing methods for cardiac medical images.Secondly,fractal analysis has been applied to quantify the complexity of left ventricular myocardial trabeculae.Inspired by this method,the paper applies fractal analysis to the quantitative analysis of the pectinate muscle of left atrial appendage.Anisotropic filtering is applied to denoise cardiac CT images.Due to the indistinct boundary between the left atrial appendage and the left atrium,the brensenham algorithm is used to generate the boundary line between them.Based on this,region growing is used for left atrial appendage segmentation.Since the pectinate muscle and thrombus signals are similar in CT images,some adjustments are made to the initial segmentation results.Then,edge detection is performed on the segmented left atrial appendage,and the box dimension method is used to quantitatively calculate the pectinate muscle of the left atrial appendage.Statistical analysis and comparisons are conducted on normal and thrombus groups,and the results show that the fractal dimension values have considerable potential in distinguishing between the normal and thrombus groups.Thirdly,the paper delves into post-processing and quantitative analysis methods for cardiac MR-DTI,including myocardial distortion correction,image registration,characteristic parameter calculation,and tensor visualization.The hyperelastic susceptibility artifact correction method,which is mostly applied to the brain,is used to the correction of myocardial distortion due to the eddy current effect of the magnetic field.It is verified that the same echo time acquisition needs to be used to counteract the inhomogeneity of the magnetic field.For the displacement of myocardium in multiple diffusion directions due to heartbeat motion,the rigid alignment using translational transformation and the non-rigid alignment method based on parametric total variation regularization are used for image registration,which improves the accuracy of feature parameter calculation.To meet the needs of clinicians for quantitative DTI analysis of patients with myocardial infarction,the calculation methods of the additional feature parameters HAg,LHM,CM and RHM were studied and validated on healthy volunteers and patients with myocardial infarction based on the calculation of the characteristic parameter helix angle.Four glyphs based on ellipsoid,cube,cylinder and superquadratic ellipsoid are employed in the visualization of diffusion tensor.Clinicians can observe the abnormal changes of myocardial fiber structure more visually.Finally,to facilitate batch processing of data,improve usability and efficiency,a software tool for processing cardiac CT data is designed,which allows clinicians to conveniently perform quantitative analysis of the pectinate muscle of left atrial appendage.Additionally,in a software tool for processing cardiac MR-DTI data,functions such as registration processing,parameter calculation,and visualization are added to enhance the functionality of the software,thereby assisting clinicians in disease diagnosis and clinical research more effectively.