Automated Cell Segmentation Method for Extracting Cell Information From Phase Contrast Microscopy of a Microfluidic Device

2015 ◽  
Author(s):  
Masoud Khabiry ◽  
Nader Jalili
Keyword(s):  
Microfluidic Device ◽  
Phase Contrast ◽  
Image Data ◽  
Cell Segmentation ◽  
Shear Stresses ◽  
Phase Contrast Microscopy ◽  
Technological Advancement ◽  
Microscopy Imaging ◽  
Contrast Microscopy ◽  
High Level

Technological advancement in cell and microscopy imaging generate a high demand for complex image analysis techniques. Image processing techniques are powerful tools to assess the images and extract information for quantization and evaluation of regions. It is crucial to develop advanced automatic cell segmentation methods with capability to analyze image data with high level of accuracy. In this paper, a quantitative technique is developed to count the number of cells from phase contrast microscopy. The technique selects region of interests (ROI) in the cell images and it measures the cell locations within ROI in a specific microfluidic device. The cell location is an important factor which will affect the flow pattern in the microfluidic device and consequently induced shear stresses.

scholarly journals Cell-sensitive phase contrast microscopy imaging by multiple exposures

2015 ◽  
Vol 25 (1) ◽  
pp. 111-121 ◽  
Author(s):  
Zhaozheng Yin ◽  
Hang Su ◽  
Elmer Ker ◽  
Mingzhong Li ◽  
Haohan Li
Keyword(s):  
Phase Contrast ◽  
Phase Contrast Microscopy ◽  
Microscopy Imaging ◽  
Multiple Exposures ◽  
Sensitive Phase ◽  
Contrast Microscopy

scholarly journals Studies on Antibody-Producing Cells by Hemolytic Plaque Method and Phase Contrast Microscopy

Blood ◽  
1969 ◽  
Vol 33 (2) ◽  
pp. 149-158
Author(s):  
IRIMAJIRI KIYOHIRO ◽  
ATSUSHI HORIOCHI ◽  
YUJI OKAMOTO ◽  
ICHITA AMAKI
Keyword(s):  
Lymph Node ◽  
Plasma Cell ◽  
Thoracic Duct ◽  
Phase Contrast ◽  
Early Stage ◽  
Phase Contrast Microscopy ◽  
Whole Process ◽  
Contrast Microscopy ◽  
Mature Lymphocyte ◽  
High Level

Abstract The numbers of the PFC in the thoracic duct and lymph node of the immunized rabbit were counted by the hemolytic plaque method and the morphologic features of the PFC in the lymph node were simultaneously observed by phase contrast microscopy. The number of the PFC in the lymph node maintained a relatively high level on the fourth to seventh days, and then rose to attain the maximum on the ninth to thirteenth days, and then decreased. The number of the PFC in the thoracic duct reached the maximum on the fourth day after the immunization and then dropped rapidly. The mechanism was discussed. From morphologic observations, the PFC were classified into lymphogonia, lymphoblast, basophilic mature lymphocyte and plasma cell. The lymphogonia and lymphoblast, which belonged to immature lymphocyte series, were frequent in the early stage and the plasma cell was increased in the later stage of the immunization. The basophilic mature lymphocyte constituted more than 55 per cent through the whole process and were most frequent of the plaque forming cells.

Analysis and Simulation of Mid-Energy X-Ray Grating Phase Contrast Microscopy Imaging

2011 ◽  
Vol 31 (10) ◽  
pp. 1034001
Author(s):  
黄建衡 Huang Jianheng ◽  
林丹樱 Lin Danying ◽  
刘振伟 Liu Zhenwei ◽  
杨强 Yang Qiang ◽  
郭金川 Guo Jinchuan ◽  
...  
Keyword(s):  
Phase Contrast ◽  
Phase Contrast Microscopy ◽  
Microscopy Imaging ◽  
X Ray ◽  
Contrast Microscopy

Cell Segmentation in Phase Contrast Microscopy by Constrained Optimization

2015 ◽  
Vol 6 (1) ◽  
pp. 36-47
Author(s):  
Enkhbolor Adiya ◽  
Bouasone Vongphachah ◽  
Alaaddin Al-Shidaifat ◽  
Enkhbat Rentsen ◽  
Heung-Kook Choi
Keyword(s):  
Phase Contrast ◽  
Projection Algorithm ◽  
Cell Segmentation ◽  
Experimental Result ◽  
Phase Contrast Microscopy ◽  
Segmentation Process ◽  
Minimum Solution ◽  
Contrast Microscopy ◽  
A Cell ◽  
Microscopy Images

Utilization of automatic cell segmentation process is difficult to identify in a cell due to halo and shade-off distortions when observing the phase contrast microscopy images. Therefore, it is an important step to restore artifact-free images made ready for segmentation process. The main focus of this paper is to define a gradient projection algorithm to restore images based on the minimization problem of quadratic objective function with non-negative constraints. The proposed algorithm converges to a global minimum solution independent on initialization. The experimental result shows that the proposed algorithm can restore artifact-free images, which could produce high quality segmentation results using a simple thresholding method.

scholarly journals Cell Segmentation of 2D Phase-Contrast Microscopy Images with Deep Learning Method

2019 ◽  
Author(s):  
Aydin Ayanzadeh ◽  
Huseyin Onur Yagar ◽  
Ozden Yalcin Ozuysal ◽  
Devrim Pesen Okvur ◽  
Behcet Ugur Toreyin ◽  
...  
Keyword(s):  
Deep Learning ◽  
Phase Contrast ◽  
Cell Segmentation ◽  
Phase Contrast Microscopy ◽  
Learning Method ◽  
Contrast Microscopy ◽  
Microscopy Images
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