scholarly journals Analysis of Glomerular Filtration Rate in Ischemic Cerebrovascular Diseases under the Magnetic Resonance Angiography Image Segmentation Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Yong Ding ◽  
Yuebin Liu ◽  
Cong Peng ◽  
Huanmei Wang ◽  
Yuqin Xu ◽  
...  
Keyword(s):  
Blood Vessel ◽  
Fuzzy Clustering ◽  
Clustering Algorithm ◽  
Vessel Segmentation ◽  
Segmentation Algorithm ◽  
Arterial Stenosis ◽  
Blood Vessel Segmentation ◽  
Fuzzy Clustering Algorithm ◽  
Vessel Stenosis ◽  
Cv Model

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.

The Segmentation Algorithm for Pavement Cracking Images Based on the Improved Fuzzy Clustering

2013 ◽  
Vol 319 ◽  
pp. 362-366 ◽  
Author(s):  
Yong Xia Zuo ◽  
Guo Qiang Wang ◽  
Chun Cheng Zuo
Keyword(s):  
Fuzzy Clustering ◽  
Clustering Algorithm ◽  
Initial Position ◽  
Segmentation Algorithm ◽  
Fuzzy Clustering Algorithm

The segmentation technology of pavement cracking image is critical for identifying, quantifying and classifying pavement cracks. An improved fuzzy clustering algorithm is introduced to segment pavement cracking images. The algorithm makes no assumptions the initial position of clusters. For each value of the multiscale parameter, it obtains a corresponding hard partition. The different partitions values of the multiscale parameter indicate the structure of the image in different partitional scales. The algorithm was tested on actual pavement cracking images. We compared the results with FCM and OTSU to show that the improved fuzzy clustering algorithm can provide better crack edges.

scholarly journals An Automatic Hybrid Method for Retinal Blood Vessel Extraction

2008 ◽  
Vol 18 (3) ◽  
pp. 399-407 ◽  
Author(s):  
Yong Yang ◽  
Shuying Huang ◽  
Nini Rao
Keyword(s):  
Blood Vessel ◽  
Blood Vessels ◽  
Fuzzy Clustering ◽  
Hybrid Method ◽  
Clustering Algorithm ◽  
Retinal Images ◽  
Purification Procedure ◽  
Retinal Blood Vessel ◽  
Fuzzy Clustering Algorithm ◽  
Vessel Extraction

An Automatic Hybrid Method for Retinal Blood Vessel ExtractionThe extraction of blood vessels from retinal images is an important and challenging task in medical analysis and diagnosis. This paper presents a novel hybrid automatic approach for the extraction of retinal image vessels. The method consists in the application of mathematical morphology and a fuzzy clustering algorithm followed by a purification procedure. In mathematical morphology, the retinal image is smoothed and strengthened so that the blood vessels are enhanced and the background information is suppressed. The fuzzy clustering algorithm is then employed to the previous enhanced image for segmentation. After the fuzzy segmentation, a purification procedure is used to reduce the weak edges and noise, and the final results of the blood vessels are consequently achieved. The performance of the proposed method is compared with some existing segmentation methods and hand-labeled segmentations. The approach has been tested on a series of retinal images, and experimental results show that our technique is promising and effective.

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