Method of famous tea sprout identification and segmentation based on improved watershed algorithm

2021 ◽  
Vol 184 ◽  
pp. 106108
Author(s):  
Lei Zhang ◽  
Lang Zou ◽  
Chuanyu Wu ◽  
Jiangming Jia ◽  
Jianneng Chen
Keyword(s):  
Watershed Algorithm

Segmentation In Medical Resonance images to extract the cancerous nodule for early diagnosis on cancer

2012 ◽  
Vol 3 (2) ◽  
pp. 253-255
Author(s):  
Raman Brar
Keyword(s):  
Human Lung ◽  
Research Work ◽  
Vital Role ◽  
Watershed Algorithm ◽  
Segmentation Method ◽  
Resonance Imaging ◽  
Segmentation Methods ◽  
Segmentation Accuracy ◽  
Background Segmentation ◽  
Lung Mri

Image segmentation plays a vital role in several medical imaging programs by assisting the delineation of physiological structures along with other parts. The objective of this research work is to segmentize human lung MRI (Medical resonance Imaging) images for early detection of cancer.Watershed Transform Technique is implemented as the Segmentation method in this work. Some comparative experiments using both directly applied watershed algorithm and after marking foreground and computed background segmentation methods show the improved lung segmentation accuracy in some image cases.

scholarly journals A Novel Vegetation Point Cloud Density Tree-Segmentation Model for Overlapping Crowns Using UAV LiDAR

2021 ◽  
Vol 13 (8) ◽  
pp. 1442
Author(s):  
Kaisen Ma ◽  
Yujiu Xiong ◽  
Fugen Jiang ◽  
Song Chen ◽  
Hua Sun
Keyword(s):  
Point Cloud ◽  
Local Maximum ◽  
Window Size ◽  
Point Clouds ◽  
High Density ◽  
Watershed Algorithm ◽  
Canopy Area ◽  
Individual Trees ◽  
Optimal Window ◽  
Cloud Density

Detecting and segmenting individual trees in forest ecosystems with high-density and overlapping crowns often results in bias due to the limitations of the commonly used canopy height model (CHM). To address such limitations, this paper proposes a new method to segment individual trees and extract tree structural parameters. The method involves the following key steps: (1) unmanned aerial vehicle (UAV)-scanned, high-density laser point clouds were classified, and a vegetation point cloud density model (VPCDM) was established by analyzing the spatial density distribution of the classified vegetation point cloud in the plane projection; and (2) a local maximum algorithm with an optimal window size was used to detect tree seed points and to extract tree heights, and an improved watershed algorithm was used to extract the tree crowns. The proposed method was tested at three sites with different canopy coverage rates in a pine-dominated forest in northern China. The results showed that (1) the kappa coefficient between the proposed VPCDM and the commonly used CHM was 0.79, indicating that performance of the VPCDM is comparable to that of the CHM; (2) the local maximum algorithm with the optimal window size could be used to segment individual trees and obtain optimal single-tree segmentation accuracy and detection rate results; and (3) compared with the original watershed algorithm, the improved watershed algorithm significantly increased the accuracy of canopy area extraction. In conclusion, the proposed VPCDM may provide an innovative data segmentation model for light detection and ranging (LiDAR)-based high-density point clouds and enhance the accuracy of parameter extraction.

A hybrid 3D watershed algorithm incorporating gradient cues and object models for automatic segmentation of nuclei in confocal image stacks

Cytometry ◽  
2003 ◽  
Vol 56A (1) ◽  
pp. 23-36 ◽  
Author(s):  
Gang Lin ◽  
Umesh Adiga ◽  
Kathy Olson ◽  
John F. Guzowski ◽  
Carol A. Barnes ◽  
...  
Keyword(s):  
Automatic Segmentation ◽  
Watershed Algorithm ◽  
Confocal Image ◽  
Object Models

scholarly journals Universal void density profiles from simulation and SDSS

2014 ◽  
Vol 11 (S308) ◽  
pp. 542-545 ◽  
Author(s):  
S. Nadathur ◽  
S. Hotchkiss ◽  
J. M. Diego ◽  
I. T. Iliev ◽  
S. Gottlöber ◽  
...  
Keyword(s):  
Fundamental Property ◽  
Watershed Algorithm ◽  
Void Size ◽  
Self Similarity ◽  
Density Profiles ◽  
Watershed Transform ◽  
Future Studies ◽  
Main Galaxy ◽  
Wide Range ◽  
Self Similar

AbstractWe discuss the universality and self-similarity of void density profiles, for voids in realistic mock luminous red galaxy (LRG) catalogues from the Jubilee simulation, as well as in void catalogues constructed from the SDSS LRG and Main Galaxy samples. Voids are identified using a modified version of the ZOBOV watershed transform algorithm, with additional selection cuts. We find that voids in simulation areself-similar, meaning that their average rescaled profile does not depend on the void size, or – within the range of the simulated catalogue – on the redshift. Comparison of the profiles obtained from simulated and real voids shows an excellent match. The profiles of real voids also show auniversalbehaviour over a wide range of galaxy luminosities, number densities and redshifts. This points to a fundamental property of the voids found by the watershed algorithm, which can be exploited in future studies of voids.

scholarly journals Automatic segmentation of foveal avascular zone based on adaptive watershed algorithm in retinal optical coherence tomography angiography images

2021 ◽  
Author(s):  
Jian Liu ◽  
Shixin Yan ◽  
Nan Lu ◽  
Dongni Yang ◽  
Chunhui Fan ◽  
...  
Keyword(s):  
Optical Coherence Tomography ◽  
Healthy Subjects ◽  
Optical Coherence Tomography Angiography ◽  
Automatic Segmentation ◽  
Qualitative Evaluation ◽  
Foveal Avascular Zone ◽  
Watershed Algorithm ◽  
Strong Positive Correlation ◽  
Optical Coherence ◽  
Avascular Zone

The size and shape of the foveal avascular zone (FAZ) have a strong positive correlation with several vision-threatening retinovascular diseases. The identification, segmentation and analysis of FAZ are of great significance to clinical diagnosis and treatment. We presented an adaptive watershed algorithm to automatically extract FAZ from retinal optical coherence tomography angiography (OCTA) images. For the traditional watershed algorithm, “over-segmentation” is the most common problem. FAZ is often incorrectly divided into multiple regions by redundant “dams”. This paper analyzed the relationship between the “dams” length and the maximum inscribed circle radius of FAZ, and proposed an adaptive watershed algorithm to solve the problem of “over-segmentation”. Here, 132 healthy retinal images and 50 diabetic retinopathy (DR) images were used to verify the accuracy and stability of the algorithm. Three ophthalmologists were invited to make quantitative and qualitative evaluations on the segmentation results of this algorithm. The quantitative evaluation results show that the correlation coefficients between the automatic and manual segmentation results are 0.945 (in healthy subjects) and 0.927 (in DR patients), respectively. For qualitative evaluation, the percentages of “perfect segmentation” (score of 3) and “good segmentation” (score of 2) are 99.4% (in healthy subjects) and 98.7% (in DR patients), respectively. This work promotes the application of watershed algorithm in FAZ segmentation, making it a useful tool for analyzing and diagnosing eye diseases.

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