Hydrocephalic cerebrospinal fluid flowing rotationally with pulsatile boundaries:A mathematical simulation of the thermodynamical approach

To study the kinematics of flow rate and ventricular dilatation,an analytical perturbation approach of hydrocephalus has been devised.This research provides a comprehensive investigation of the characteristics of cerebrospinal fluid(CSF)flow and pressure in a hydrocephalic patient.The influence of hydrocephalic CSF,flowing rotationally with realistic dynamical characteristics on pulsatile boundaries of subarachnoid space,was demonstrated using a nonlinear controlling system of CSF.An analytical perturbation method of hydrocephalus has been developed to investigate the biomechanics of fluid flow rate and the ventricular enlargement.In this paper presents a detailed analysis of CSF flow and pressure dynamics in a hydrocephalic patient.It was elaborated with a nonlinear governing model of CSF to show the influence of hydrocephalic CSF,flowing rotationally with realistic dynamical behaviors on pulsatile boundaries of subarachnoid space.In accordance with the suggested model,the elasticity factor changes depending on how much a porous layer,in this case the brain parenchyma,is stretched.It was improved to include the relaxation of internal mechanical stresses for various perturbation orders,modelling the potential plasticity of brain tissue.The initial geometry that was utilised to create the framework of CSF with pathological disease hydrocephalus and indeed the output of simulations using this model were compared to the actual progression of ventricular dimensions and shapes in patients.According to this observation,the non-linear and elastic mechanical phenomena incorporated into the current model are probably true.Further modelling of ventricular dilation at a normal pressure may benefit from the existence of a valid model whose parameters approximate genuine mechanical characteristics of the cerebral cortex.

Computer-aided recognition and assessment of a porous bioelastomer in ultrasound images for regenerative medicine applications

It is difficult to use a single edge operator in image processing to extract continuous and accurate contours of a porous bioelastomer due to the fuzzy boundary and complex background in ultrasound images.To solve this problem,this paper proposes a joint algorithm for bioelastomer contour detection and a texture feature extraction method for monitoring the degradation performance of bioelastomers.First,the mean-shift clustering method is utilized to obtain the clustering feature information of bioelastomers and native tissue from manually segmented images,and this information is used as the initial information in the image binarization algorithm for image partitioning.Second,Otsu's thresholding method and mathematical morphology are applied in the process of image binarization.Finally,the Canny edge detector is employed to extract the complete bioelastomers contour from the binary image.To verify the robustness of the proposed joint algorithm,the results using the proposed joint algorithm,where mean-shift clustering is replaced with k-means clustering are also obtained.The proposed joint algorithm based on mean-shift clustering outperforms the joint algorithm based on k-means clustering,as well as algorithms that directly apply the Canny,Sobel and Laplacian methods.Texture feature extraction is based on the computer-aided recognition of bioelastomers.The region of interest(ROI)is set in the scaffold region,and the first-order statistical features and second-order statistical features of the greyscale values of the ROI are extracted and analysed.The proposed joint algorithm can not only extract ideal bioelastomers contours from ultrasound images but also provide valuable feedback on the degradation behaviour of bioelastomers at implant sites.

Automatic Identification Fingerprint Based on Machine Learning Method

The fingerprint identification technology has been developed and applied effectively to security systems in financial transactions,personal information security,national security,and other fields.In this paper,we proposed the development of a fingerprint identification system based on image processing methods that clarify fingerprint contours,using machine learning methods to increase processing speed and increase the accuracy of the fingerprint identification process.The identification system consists of the following main steps:improving image quality and image segmentation to identify the fingerprint area,extracting features,and matching the database.The accuracy of the system reached 97.75%on the mixed high-and low-quality fingerprint database.