Risk Factors of Restroke in Patients with Lacunar Cerebral Infarction Using Magnetic Resonance Imaging Image Features under Deep Learning Algorithm

Abstract

This study was aimed to explore the magnetic resonance imaging (MRI) image features based on the fuzzy local information C-means clustering (FLICM) image segmentation method to analyze the risk factors of restroke in patients with lacunar infarction. In this study, based on the FLICM algorithm, the Canny edge detection algorithm and the Fourier shape descriptor were introduced to optimize the algorithm. The difference of Jaccard coefficient, Dice coefficient, peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), running time, and segmentation accuracy of the optimized FLICM algorithm and other algorithms when the brain tissue MRI images were segmented was studied. 36 patients with lacunar infarction were selected as the research objects, and they were divided into a control group (no restroke, 20 cases) and a stroke group (restroke, 16 cases) according to whether the patients had restroke. The differences in MRI imaging characteristics of the two groups of patients were compared, and the risk factors for restroke in lacunar infarction were analyzed by logistic multivariate regression. The results showed that the Jaccard coefficient, Dice coefficient, PSNR value, and SSIM value of the optimized FLICM algorithm for segmenting brain tissue were all higher than those of other algorithms. The shortest running time was 26 s, and the highest accuracy rate was 97.86%. The proportion of patients with a history of hypertension, the proportion of patients with paraventricular white matter lesion (WML) score greater than 2 in the stroke group, the proportion of patients with a deep WML score of 2, and the average age of patients in the stroke group were much higher than those in the control group ( P < 0.05 ). Logistic multivariate regression showed that age and history of hypertension were risk factors for restroke after lacunar infarction ( P < 0.05 ). It showed that the optimized FLICM algorithm can effectively segment brain MRI images, and the risk factors for restroke in patients with lacunar infarction were age and hypertension history. This study could provide a reference for the diagnosis and prognosis of lacunar infarction.

Value of Magnetic Resonance Images and Magnetic Resonance Spectroscopy in Diagnosis of Brain Tumors under Fuzzy C‐Means Algorithm

“…It was inappropriate to use Euclidean distance in this case, so the kernel function was introduced to measure the distance between pixels in the space. The low-dimensional space was mapped to the high-dimensional space, and the complex nonlinear issue was transformed into the linear issue of kernel space [ 13 , 14 ], enhancing the noise immunity of the algorithm. The steps of improving the FCM algorithm were given as follows.…”

A comprehensive review for artificial intelligence on neuroimaging in rehabilitation of ischemic stroke

“…The results showed that, under the same noise conditions, the optimized FLICM algorithm exhibited higher Jaccard coefficient, Dice coefficient, PSNR, and SSIM values when segmenting brain tissue compared to other algorithms. Additionally, age and a history of hypertension were identified as risk factors for recurrent stroke after lacunar infarction ( 86 ).…”

Quantitative Analysis of Multimodal MRI Markers and Clinical Risk Factors for Cerebral Small Vessel Disease Based on Deep Learning

“…Compared to computed tomography (CT) scans, MRI scans offer greater sensitivity, and many imaging features can only be detected through MRI. 2 Notably, there are multiple radiological markers for CSVD, including white matter hyperintensities (WMH), recent small subcortical infarcts (RSSI), lacunar infarcts (LI), enlarged perivascular spaces (EPVS), and cerebral microbleeds (CMB). 3 Different imaging markers rely on different MRI sequences for detection.…”