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Related Concept Videos

Mesenchymal Stem Cells01:19

Mesenchymal Stem Cells

Mesenchymal stem cells (MSCs) are adult stem cells that can differentiate into most connective tissue cell types, except for hematopoietic cells, depending upon the source of MSCs. For example, bone-marrow-derived MSCs (BM-MSCs) can differentiate into osteocytes, hepatocytes, and pancreatic and neuronal cells. MSCs can be isolated from various sources such as bone marrow, placenta, adipose tissue, teeth, and Wharton’s jelly, a gelatinous substance in the umbilical cord. The ease of their access...

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Isolation and Identification of Mesenchymal Stem Cells Derived from Adipose Tissue of Sprague Dawley Rats
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[Research on mesenchymal stem cells image segmentation based on morphology].

Jian Zhang1, Yuming Zhao

  • 1Institute of Image Processing & Pattern Recognition, Shanghai Jiaotong University, Shanghai 200240, China.

Sheng Wu Yi Xue Gong Cheng Xue Za Zhi = Journal of Biomedical Engineering = Shengwu Yixue Gongchengxue Zazhi
|September 29, 2007
PubMed
Summary
This summary is machine-generated.

This study presents a new method for segmenting mesenchymal stem cells (MSCs). The developed technique accurately identifies and quantifies cell populations for research.

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Area of Science:

  • Biomedical Engineering
  • Cell Biology
  • Image Analysis

Background:

  • Mesenchymal stem cells (MSCs) research is growing in popularity.
  • Accurate segmentation of MSCs is crucial for quantitative analysis.
  • Existing segmentation methods may require improvement for complex biological images.

Purpose of the Study:

  • To develop and validate an automated method for segmenting mesenchymal stem cells (MSCs).
  • To enable accurate calculation of cell area and number from microscopic images.
  • To provide a robust image preprocessing pipeline for MSC analysis.

Main Methods:

  • Application of the top-bottom hat algorithm from gray morphology for image preprocessing.
  • Utilizing a structure function with a radius of 30 for morphological operations.
  • Employing a two-thresholding technique to obtain a binary image distinguishing cells from the background.
  • Implementing morphological algorithms for noise reduction and image refinement.

Main Results:

  • Successful preprocessing of microscopic images to enhance cell visibility.
  • Effective removal of noise, leading to a clear distinction between foreground (cells) and background.
  • Generation of a binary image suitable for automated cell counting and area measurement.
  • Demonstrated ease in differentiating cellular structures after applying the described algorithms.

Conclusions:

  • The proposed image processing technique effectively segments mesenchymal stem cells (MSCs).
  • The method allows for reliable quantification of cell area and number.
  • This approach offers a valuable tool for researchers studying MSCs.