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Stereological Estimation of Cholinergic Fiber Length in the Nucleus Basalis of Meynert of the Mouse Brain
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[A nucleus location method based on distance estimation].

Wenhao Sun1, Fuxiang Lu1, Yide Ma2

  • 1School of Information Science and Engineering, Lanzhou University, Lanzhou 730000, P.R.China.

Sheng Wu Yi Xue Gong Cheng Xue Za Zhi = Journal of Biomedical Engineering = Shengwu Yixue Gongchengxue Zazhi
|June 26, 2018
PubMed
Summary
This summary is machine-generated.

A new distance estimation method simplifies and improves nucleus location in hematoxylin-eosin (HE) stained images. This approach enhances efficiency and accuracy for automated HE image analysis systems.

Keywords:
boundary matrixdistance estimationlocalization of nucleimatrix sequence of distance rough estimatingmedical image processingsingle target location

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Area of Science:

  • Digital pathology
  • Computational imaging
  • Biomedical image analysis

Background:

  • Accurate nucleus localization is crucial for analyzing hematoxylin-eosin (HE) stained tissue sections.
  • Current methods for nucleus detection in clustered regions can be complex and time-consuming.
  • There is a need for more efficient and precise automated nucleus identification techniques.

Purpose of the Study:

  • To develop a novel, simplified, and accurate method for locating nuclei in HE stained images.
  • To improve the efficiency of nucleus detection, particularly in clumped cell populations.
  • To enhance the performance of automated HE image analysis systems.

Main Methods:

  • A new method based on distance estimation is proposed.
  • The method utilizes matrix sequence of distance rough estimating (MSDRE).
  • It leverages the principle that the center of a convex region is farthest from its boundary to pinpoint nuclei.

Main Results:

  • Achieved a precision of 95.26% in nucleus localization.
  • Demonstrated high efficiency with a processing time of 1.54 seconds per thousand nuclei.
  • Outperformed mainstream methods in recognizing nucleus clump samples.

Conclusions:

  • The proposed distance estimation method offers a simpler, more efficient, and accurate approach to nucleus location in HE images.
  • This method improves the real-time performance of automated HE image analysis.
  • It has the potential to significantly advance related research and applications in digital pathology.