Modified Multi - Scale Morphological Watershed Segmentation Algorithm of 2d Images Using Hill Climbing Techniques

2012 ◽  
Vol 1 (1) ◽  
pp. 33-38
Author(s):  
R. Chithra Devi ◽  
◽  
R. Ananda Devi ◽  
T. Saravana Kumar ◽  
◽  
...  
Keyword(s):  
Segmentation Algorithm ◽  
Hill Climbing ◽  
Watershed Segmentation ◽  
Multi Scale ◽  
2D Images

scholarly journals Pine wilt disease detection in high-resolution UAV images using object-oriented classification

2021 ◽  
Author(s):  
Zhao Sun ◽  
Yifu Wang ◽  
Lei Pan ◽  
Yunhong Xie ◽  
Bo Zhang ◽  
...  
Keyword(s):  
High Resolution ◽  
Large Scale ◽  
Digital Camera ◽  
Object Oriented ◽  
Pine Wilt Disease ◽  
Wilt Disease ◽  
Segmentation Algorithm ◽  
Tree Crown ◽  
Multi Scale ◽  
Pine Wilt

AbstractPine wilt disease (PWD) is currently one of the main causes of large-scale forest destruction. To control the spread of PWD, it is essential to detect affected pine trees quickly. This study investigated the feasibility of using the object-oriented multi-scale segmentation algorithm to identify trees discolored by PWD. We used an unmanned aerial vehicle (UAV) platform equipped with an RGB digital camera to obtain high spatial resolution images, and multi-scale segmentation was applied to delineate the tree crown, coupling the use of object-oriented classification to classify trees discolored by PWD. Then, the optimal segmentation scale was implemented using the estimation of scale parameter (ESP2) plug-in. The feature space of the segmentation results was optimized, and appropriate features were selected for classification. The results showed that the optimal scale, shape, and compactness values of the tree crown segmentation algorithm were 56, 0.5, and 0.8, respectively. The producer’s accuracy (PA), user’s accuracy (UA), and F1 score were 0.722, 0.605, and 0.658, respectively. There were no significant classification errors in the final classification results, and the low accuracy was attributed to the low number of objects count caused by incorrect segmentation. The multi-scale segmentation and object-oriented classification method could accurately identify trees discolored by PWD with a straightforward and rapid processing. This study provides a technical method for monitoring the occurrence of PWD and identifying the discolored trees of disease using UAV-based high-resolution images.

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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