U-net gland cell image segmentation method combined with spatial attention

2021 ◽  
Author(s):  
Mingmin Gong ◽  
Aijun Chen ◽  
Hao Feng
Keyword(s):  
Image Segmentation ◽  
Spatial Attention ◽  
Gland Cell ◽  
Segmentation Method ◽  
Cell Image

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 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

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

Segmentation of Complex Microscopic Cell Image Based on Contourlet and Level Set

2012 ◽  
Vol 429 ◽  
pp. 298-302
Author(s):  
Zhi Gang Chen ◽  
Ai Hua Chen ◽  
Yue Li Cui
Keyword(s):  
Image Segmentation ◽  
Level Set ◽  
Recognition System ◽  
Automatic Recognition ◽  
Accurate Data ◽  
Original Image ◽  
Segmentation Method ◽  
Urinary Sediment ◽  
Cell Image ◽  
Sediment Particles

In order to more precisely segment complex microscopic cell image, a new image segmentation method by combination of coarse segmentation and fine segmentation is proposed. Firstly, the coutourlet transform and morphology are used to segment original image coarsely and get the subimages that include the particles. Then ,the Level Set method is employed to locate edge of the particles precisely. The method provides more accurate data for complex microscopic cell automatic recognition system. Taking example for complex urinary sediment image, the experiment results show that the method can segment urinary sediment images effectively and precisely and increasing the performance of urinary sediment particles recognition.

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