scholarly journals Diffusion Kurtosis-Based Brain Image Segmentation for the Structural Remodeling of White Matter in Patients with Hypoxic-Ischemic Encephalopathy

2021 ◽  
Vol 2021 ◽  
pp. 1-6
Author(s):  
Yongkun Kang ◽  
Chuang Sun ◽  
Chao Yang ◽  
Honghai Chen
Keyword(s):  
Image Segmentation ◽  
Internal Capsule ◽  
Segmentation Algorithm ◽  
Hypoxic Ischemic Encephalopathy ◽  
Structural Remodeling ◽  
Posterior Limb ◽  
Affected Side ◽  
Diffusion Kurtosis ◽  
Ischemic Encephalopathy ◽  
Image Segmentation Algorithm

The aim was to explore the application value of brain image segmentation algorithm based on diffusion kurtosis imaging (DKI) in the structural remodeling of white matter (WM) in patients with hypoxic-ischemic encephalopathy (HIE). 120 patients with leukoencephalopathy and hypoxic-ischemic encephalopathy were selected as the research objects. Their heads were scanned by conventional magnetic resonance imaging (MRI) and DKI sequence. Besides, DKI based on the image segmentation algorithm was applied to process DKI images, DKE software was employed to obtain the values of fractional anisotropy (FA) and mean kurtosis (MK), and the differences in FA and MK were compared between acute and chronic phases. The results showed that the proposed algorithm could realize the best values of Jaccard similarity (JS) and Dice similarity coefficient (DSC) in WM, gray matter (GM), and cerebrospinal fluid (CSF), and the segmentation accuracy was better than other algorithms. FA values at the acute and chronic phases were compared in the area around lesions (0.421 ± 0.065 vs. 0.454 ± 0.052), the posterior limb of internal capsule on the affected side (0.498 ± 0.027 vs. 0.504 ± 0.046), and the pedunculus cerebri on the affected side (0.558 ± 0.038 vs. 0.568 ± 0.042), and the differences were statistically substantial ( P < 0.05 ). Moreover, MK values at the acute and chronic phases were also compared in the area around lesions (1.362 ± 0.098 vs. 1.407 ± 0.077), the centrum semiovale on the affected side (1.305 ± 0.102 vs. 1.343 ± 0.076), the posterior limb of internal capsule on the affected side (1.338 ± 0.543 vs. 1.382 ± 0.076), and the pedunculus cerebri on the affected side (1.329 ± 0.089 vs. 1.398 ± 0.099), showing a statistical meaning ( P < 0.05 ). The results indicated that the changes of FA and MK were related to the structural remodeling of WM. The DKI image segmentation algorithm could be applied in the diagnosis of leukoencephalopathy in patients with hypoxic-ischemic encephalopathy, and DKI technology was of great significance for the research of structural remodeling of WM.

scholarly journals A smoke image segmentation algorithm based on rough set and region growing

2019 ◽  
Vol 65 (No. 8) ◽  
pp. 321-329
Author(s):  
Haitao Wang ◽  
Yanli Chen
Keyword(s):  
Image Segmentation ◽  
Rough Set ◽  
Region Growing ◽  
Recall Rate ◽  
Segmentation Algorithm ◽  
Colour Space ◽  
Seed Point ◽  
Black Smoke ◽  
Growth Method ◽  
Image Segmentation Algorithm

Because the image fire smoke segmentation algorithm can not extract white, gray and black smoke at the same time, a smoke image segmentation algorithm is proposed by combining rough set and region growth method. The R component of the image is extracted in the RGB colour space, the roughness histogram is constructed according to the statistical histogram of the R component, and the appropriate valley value in the roughness histogram is selected as the segmentation threshold, the image is roughly segmented. Relative to the background image, the smoke belongs to the motion information, and the motion region is extracted by the interframe difference method to eliminate static interference. Smoke has a unique colour feature, a smoke colour model is created in the RGB colour space, the motion disturbances of similar colour are removed and the suspected smoke areas are obtained. The seed point is selected in the region, and the region is grown on the result of rough segmentation, the smoke region is extracted. The experimental results show that the algorithm can segment white, gray and black smoke at the same time, and the irregular information of smoke edges is relatively complete. Compared with the existing algorithms, the average segmentation accuracy, recall rate and F-value are increased by 19%, 21.5% and 20%, respectively.<br /><br />

A Fast 2-D Otsu lung tissue image segmentation algorithm based on improved PSO

2021 ◽  
Vol 80 ◽  
pp. 103527
Author(s):  
Yanqiao Zhao ◽  
Xiaoyang Yu ◽  
Haibin Wu ◽  
Yong Zhou ◽  
Xiaoming Sun ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Lung Tissue ◽  
Segmentation Algorithm ◽  
Image Segmentation Algorithm ◽  
Improved Pso
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