Abstract
In order to discuss the segmentation effect of the magnetic resonance angiography (MRA) image segmentation algorithm based on the fuzzy clustering algorithm and DR-CV model and the prognostic value of glomerular filtration rate (GFR) in the ischemic cerebrovascular disease (ICVD), a total of 178 patients who were admitted to the hospital and received MRA due to ICVD were selected as the research objects of this study. Blood vessel segmentation was performed on the MRA image by fuzzy clustering algorithm and DR-CV model, and all patients were divided into a control group (group A), a single-vessel stenosis group (group B), a two-vessel stenosis group (group C), and a multiple-vessel stenosis group (group D). The GFR was estimated by using the dietary modification equation for kidney disease, and the correlation between GFR and the severity of arterial stenosis in patients with ICVD was analyzed. It was found that the results of the Dice similarity index (DSI) of the MRA image blood vessel segmentation algorithm based on the fuzzy clustering algorithm and the integrated model of boundary and regional information (DR-CV model) were all above 85%. The age and GFR values of the four groups of patients were significantly different ( P < 0.05). The proportions of patients in groups C and D in the group with low DFR were significantly different from those in groups A and B ( P < 0.01); the proportions of patients in groups A and B in the high-level GFR group had extremely significant differences compared with group D ( P < 0.01). Age, GFR, total cholesterol (TC), and high-density lipoprotein-C (HDL-C) were correlated with the degree of arterial stenosis ( P < 0.05). It showed that the segmentation effect of MRA image blood vessel segmentation algorithm based on the fuzzy clustering algorithm and DR-CV model was better, and the GFR level can be used as an independent risk factor for the ICVD.
Highlights
ICVD is a common disease in clinical practice at present. e main cause of the disease is stenosis or occlusion of blood vessels in the brain, which eventually causes localized cerebral ischemic necrosis
Among the fuzzy clustering algorithms, the more mature one was the fuzzy C-mean (FCM) algorithm. e objective function of FCM was shown in equation (1), where N was the set of all pixels of the image, the total number of pixels was n; μjk was the membership of the jth pixel belonging to the kth group, and the Ck 1 μjk 1 was satisfied. xj was the gray value of the jth pixel, and vk was the center gray of the kth group
magnetic resonance angiography (MRA) Image Segmentation Results. e MRA image of the target area marked by the doctor was obtained and taken as the gold standard of the doctor’s manual standard, as shown in Figure 4. e segmented MRA image was obtained after segmentation
Summary
ICVD is a common disease in clinical practice at present. e main cause of the disease is stenosis or occlusion of blood vessels in the brain, which eventually causes localized cerebral ischemic necrosis. DSA is currently the gold standard for diagnosing the abnormal blood vessel morphology, but because of its high diagnostic cost, invasiveness, radiation exposure, and complicated and time-consuming operations, it is not suitable for initial screening and census [1]. The doctors are required to manually outline the contours of blood vessels in the area of interest (AOI) for image segmentation in the clinical diagnosis. The tracking method cannot perform automatic segmentation [4]; the segmentation result of the artificial intelligence algorithm is more accurate, the algorithm is more complicated [5]; the segmentation speed of the neural network algorithm is faster, but it requires manual interaction during the calculation process [6]
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