Research on Image Segmentation Technology with Tissue Section Cell Segmentation Algorithm

2014 ◽  
Vol 1046 ◽  
pp. 88-91
Author(s):  
Chun Bao Huo ◽  
Shuai Tong ◽  
Li Hui Zhao ◽  
Xiang Yun Li
Keyword(s):  
Image Segmentation ◽  
Tissue Section ◽  
Threshold Value ◽  
Cell Segmentation ◽  
Gray Level ◽  
Segmentation Method ◽  
Cell Edge ◽  
Cell Image ◽  
Original Picture ◽  
Maximum Interval

Generally, the effect of cell image that segmented via the threshold value method is not ideal generally; the found cell boundary cannot conform to the cell edge in the original picture well. In this paper, the threshold value segmentation method is improved; apply the judging criterion of gray level difference maximum interval to be the minimum, and conduct secondary treating on the image, and the image’s segmentation effect is more ideal.

scholarly journals Applying Onicescu Information Energy for Gray Level Image Segmentation

2020 ◽  
Author(s):  
Vasile Patrascu
Keyword(s):  
Image Segmentation ◽  
Local Maximum ◽  
Energy Calculation ◽  
Gray Level ◽  
Segmentation Method ◽  
Gray Level Image ◽  
Information Energy

This article presents a method of segmenting images with gray levels that uses Onicescu's information energy calculated in the context of the neutrosophic theory. Starting from the information energy calculation for complete neutrosophic information, it is shown how to extend its calculation for incomplete and inconsistent neutrosophic information. The segmentation method is based on calculation of thresholds for separating the gray levels using the local maximum points of the Onicescu information energy.

scholarly journals Automatic clustering method to segment COVID-19 CT images

PLoS ONE ◽  
2021 ◽  
Vol 16 (1) ◽  
pp. e0244416
Author(s):  
Mohamed Abd Elaziz ◽  
Mohammed A. A. Al-qaness ◽  
Esraa Osama Abo Zaid ◽  
Songfeng Lu ◽  
Rehab Ali Ibrahim ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Threshold Value ◽  
Ct Images ◽  
Optimal Number ◽  
Clustering Methods ◽  
Segmentation Method ◽  
Density Peaks ◽  
Automatic Clustering ◽  
Proposed Model ◽  
Density Peaks Clustering

Coronavirus pandemic (COVID-19) has infected more than ten million persons worldwide. Therefore, researchers are trying to address various aspects that may help in diagnosis this pneumonia. Image segmentation is a necessary pr-processing step that implemented in image analysis and classification applications. Therefore, in this study, our goal is to present an efficient image segmentation method for COVID-19 Computed Tomography (CT) images. The proposed image segmentation method depends on improving the density peaks clustering (DPC) using generalized extreme value (GEV) distribution. The DPC is faster than other clustering methods, and it provides more stable results. However, it is difficult to determine the optimal number of clustering centers automatically without visualization. So, GEV is used to determine the suitable threshold value to find the optimal number of clustering centers that lead to improving the segmentation process. The proposed model is applied for a set of twelve COVID-19 CT images. Also, it was compared with traditional k-means and DPC algorithms, and it has better performance using several measures, such as PSNR, SSIM, and Entropy.

scholarly journals AAW-based Cell Image Segmentation Method

2007 ◽  
Vol 14B (2) ◽  
pp. 99-106 ◽  
Author(s):  
Mi-Suk Seo ◽  
Byoung-Chul Ko ◽  
Jae-Yeal Nam
Keyword(s):  
Image Segmentation ◽  
Segmentation Method ◽  
Cell Image

scholarly journals Automatic morphological characterization of nanobubbles with a novel image segmentation method and its application in the study of nanobubble coalescence

2015 ◽  
Vol 6 ◽  
pp. 952-963 ◽  
Author(s):  
Yuliang Wang ◽  
Huimin Wang ◽  
Shusheng Bi ◽  
Bin Guo
Keyword(s):  
Image Segmentation ◽  
External Disturbance ◽  
Threshold Value ◽  
Morphological Characterization ◽  
Contour Method ◽  
Radius Of Curvature ◽  
Segmentation Method ◽  
Threshold Method ◽  
Modified Method

Nanobubbles (NBs) on hydrophobic surfaces in aqueous solvents have shown great potential in numerous applications. In this study, the morphological characterization of NBs in AFM images was carried out with the assistance of a novel image segmentation method. The method combines the classical threshold method and a modified, active contour method to achieve optimized image segmentation. The image segmentation results obtained with the classical threshold method and the proposed, modified method were compared. With the modified method, the diameter, contact angle, and radius of curvature were automatically measured for all NBs in AFM images. The influence of the selection of the threshold value on the segmentation result was discussed. Moreover, the morphological change in the NBs was studied in terms of density, covered area, and volume occurring during coalescence under external disturbance.

A Crack Segmentation Approach Using the Combination of Gray Thresholds and Fractal Feature

2012 ◽  
Vol 487 ◽  
pp. 622-626 ◽  
Author(s):  
Song Yang ◽  
Long Tan Shao ◽  
Xiao Xia Guo ◽  
Xiao Liu ◽  
Bo Ya Zhao
Keyword(s):  
Image Segmentation ◽  
Fractal Geometry ◽  
Contour Detection ◽  
Crack Identification ◽  
Gray Level ◽  
Segmentation Method ◽  
Constraint Condition ◽  
Segmentation Approach ◽  
Initial Segmentation ◽  
Crack Contour

A segmentation method of combining gray-level threshold and fractal feature for crack images is proposed, and the fractal law for the perimeter and area of the target is introduced as the constraint condition for the image segmentation of crack. At first, Otsu algorithm is used for the initial segmentation of the crack image, and then the edge of crack is optimized in accordance with fractal law. At last, boundary of crack is determined, and the final result of the image segmentation is obtained. This method makes full use of the fractal geometry law and image information, to effectively solve the problems such as crack contour detection, regional connection and cross crack identification. Several typical examples are analyzed, and the results show that this method has a good segmentation effect on crack images, and it can also be used to identify the other images which have fractal feature.

Cell Image Segmentation Method Based on Residual Block and Attention Mechanism

2020 ◽  
Vol 40 (17) ◽  
pp. 1710001
Author(s):  
张文秀 Zhang Wenxiu ◽  
朱振才 Zhu Zhencai ◽  
张永合 Zhang Yonghe ◽  
王新宇 Wang Xinyu ◽  
丁国鹏 Ding Guopeng
Keyword(s):  
Image Segmentation ◽  
Attention Mechanism ◽  
Segmentation Method ◽  
Cell Image ◽  
Residual Block
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