A new method for mesoscale eddy detection based on watershed segmentation algorithm

2014 ◽  
Author(s):  
Lijuan Qin ◽  
Qing Dong ◽  
Cunjin Xue ◽  
Xueyan Hou ◽  
Wanjiao Song
Keyword(s):  
Mesoscale Eddy ◽  
Segmentation Algorithm ◽  
New Method ◽  
Watershed Segmentation ◽  
Eddy Detection

scholarly journals Implementation of Watershed Based Image Segmentation Algorithm using Distance Transform for Grid Computing

2013 ◽  
Vol 10 (6) ◽  
pp. 1715-1719
Author(s):  
Megha Sharma ◽  
Seema Verma
Keyword(s):  
Image Analysis ◽  
Image Segmentation ◽  
Grid Computing ◽  
Segmentation Algorithm ◽  
New Method ◽  
Distance Transform ◽  
Image Information ◽  
Watershed Transform ◽  
Image Segmentation Algorithm

A new method for image segmentation using watershed transform algorithm is presented in this paper. It takes advantage of the fact that the proposed algorithm produced good results even if the same parameters are used for the standard segmentation algorithm. The proposed segmentation algorithm will be very effective for grid computing as it seems to possess specific tasks of image information and detection in order to obtain a detailed and accurate image analysis.

scholarly journals Diffusion Tensor Imaging Features of Watershed Segmentation Algorithm for Analysis of the Relationship between Depression and Brain Nerve Function of Patients with End-Stage Renal Disease

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Feng Zhu ◽  
Jiao Xu ◽  
Mei Yang ◽  
Haitao Chi
Keyword(s):  
End Stage Renal Disease ◽  
Diffusion Tensor ◽  
Segmentation Algorithm ◽  
Control Group ◽  
Nerve Function ◽  
Watershed Segmentation ◽  
Stage Renal Disease ◽  
End Stage ◽  
The Relationship ◽  
Esrd Patients

The aim of this research was to explore the relationship between depression and brain nerve function in patients with end-stage renal disease (ESRD) and long-term maintenance hemodialysis (MHD) based on watershed segmentation algorithm using diffusion tensor imaging (DTI) technology. A total of 29 ESRD patients with depression who received MHD treatment in the hemodialysis center of hospital were included as the research subjects (case group). A total of 29 healthy volunteers were recruited as the control group, and a total of 29 ESRD patients with depression and brain lesions were recruited as the control group (HC group). Within 24 h after hemodialysis, the blood biochemical indexes were collected before this DTI examination. All participants completed the neuropsychological scale (MoCA, TMT A, DST, SAS, and SDS) test. The original DTI data of all subjects were collected and processed based on watershed segmentation algorithm, and the results of automatic segmentation according to the image were evaluated as DSC = 0.9446, MPA = 0.9352, and IOU = 0.8911. Finally, the average value of imaging brain neuropathy in patients with depression in the department of nephrology was obtained. The differences in neuropsychological scale scores (PSQI, MoCA, TMTA, DST, SAS, and SDS) between the two groups were statistically significant ( P < 0.05 ). The differences of FA values in all the white matter partitions of Fu organs, except the cingulum of hippocampus (CgH) between the two groups, were statistically significant ( P < 0.05 ). ESRD and DTI quantitative detection under the guidance of watershed segmentation algorithm in MHD patients showed that ESRD patients can be early identified, so as to carry out psychological nursing as soon as possible to reduce the occurrence of depression, and then protect the brain nerve to reduce brain neuropathy.

A watershed segmentation algorithm based on an optimal marker for bubble size measurement

Measurement ◽  
2019 ◽  
Vol 138 ◽  
pp. 182-193 ◽  
Author(s):  
Hu Zhang ◽  
Zhaohui Tang ◽  
Yongfang Xie ◽  
Xiaoliang Gao ◽  
Qing Chen
Keyword(s):  
Bubble Size ◽  
Segmentation Algorithm ◽  
Size Measurement ◽  
Watershed Segmentation
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