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.
Highlights
Lacunar cerebral infarction (LCI) is a common type of ischemic stroke, and LCI patients in China account for more than 25% of cerebral infarction [1]
Analysis of the Brain Tissue magnetic resonance imaging (MRI) Image Segmentation Results Based on the fuzzy local information C-means clustering (FLICM) Algorithm. e brain tissue of the original brain MRI image (Figure 3(a)) was segmented by the FLICM algorithm optimized in this study. e segmented brain white matter image (Figure 3(b)), cerebrospinal fluid image (Figure 3(c)), and brain gray matter image (Figure 3(d)) showed that the optimized FLICM algorithm can completely segment different brain tissues from brain MRI images
Performance of Brain Tissue MRI Image Segmentation Based on the FLICM Algorithm. e optimized FLICM algorithm in this study was compared with the fuzzy C-means algorithm (FCM) and convolutional neural network (CNN) segmentation of brain tissue MRI images of the brain white matter Jaccard coefficients (Figure 4)
Summary
Lacunar cerebral infarction (LCI) is a common type of ischemic stroke, and LCI patients in China account for more than 25% of cerebral infarction [1]. At present, imaging techniques such as computed tomography (CT) and magnetic resonance imaging (MRI) are often used to diagnose patients with lacunar infarction. When CT is used to diagnose lacunar cerebral infarction, there are low-density changes in the lesion site and the boundary is unclear. It is less sensitive to the brain tissue edema caused by lacunar infarction. If the patient undergoes a CT examination less than 24 hours after the onset of the disease, the infarct focus of the brain tissue has not changed. Contrast Media & Molecular Imaging significantly at this time, so the positive rate of CT examination is low. MRI has higher tissue resolution, can clearly display early lesions, and is very sensitive to cytotoxic edema and interstitial edema
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