Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Video

Updated: Sep 4, 2025

Efficient and Consistent Generation of Retinal Pigment Epithelium/Choroid Flatmounts from Human Eyes for Histological Analysis
07:59

Efficient and Consistent Generation of Retinal Pigment Epithelium/Choroid Flatmounts from Human Eyes for Histological Analysis

Published on: October 28, 2022

2.9K

Leukocyte Segmentation Method Based on Adaptive Retinex Correction and U-Net.

Wei Chen1, Mengjing Zhu1

  • 1School of Communication and Information Engineering, Xi'an University of Science and Technology, Shaanxi 710054, China.

Computational and Mathematical Methods in Medicine
|July 14, 2022
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

Classification of Leukocytes01:30

Classification of Leukocytes

2.5K
Leukocytes are classified into two groups based on the presence or absence of cytoplasmic granules. Granular leukocytes, which contain granules, belong to the myeloid lineage and are divided into three subtypes: neutrophils, eosinophils, and basophils. These cells are roughly spherical and characterized by the granules in their cytoplasm.
Neutrophils are the most abundant type of granular leukocytes, comprising 50-70% of all leukocytes. They feature small, evenly distributed granules and a...
2.5K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Improved U-net-based leukocyte segmentation method.

Journal of biomedical optics·2023
Same author

Blood Vessel Segmentation of Fundus Retinal Images Based on Improved Frangi and Mathematical Morphology.

Computational and mathematical methods in medicine·2021
Same journal

Correction to "Mathematical Modelling of COVID-19 Transmission in Kenya: A Model with Reinfection Transmission Mechanism".

Computational and mathematical methods in medicine·2025
Same journal

RETRACTION: Ligustrazine Inhibits Lung Phosphodiesterase Activity in a Rat Model of Allergic Asthma.

Computational and mathematical methods in medicine·2025
Same journal

RETRACTION: Delivery of miR-224-5p by Exosomes from Cancer-Associated Fibroblasts Potentiates Progression of Clear Cell Renal Cell Carcinoma.

Computational and mathematical methods in medicine·2025
Same journal

RETRACTION: Empirical Analysis of the Nursing Effect of Intelligent Medical Internet of Things in Postoperative Osteoarthritis.

Computational and mathematical methods in medicine·2025
Same journal

RETRACTION: Evaluation and Analysis of the Intervention Effect of Systematic Parent Training Based on Computational Intelligence on Child Autism.

Computational and mathematical methods in medicine·2024
Same journal

RETRACTION: Humanistic Spirit Training of Medical Students Based on Multisource Medical Data Fusion.

Computational and mathematical methods in medicine·2024
See all related articles

A novel method enhances leukocyte segmentation in blood cell images using adaptive Retinex correction and U-net. This approach significantly improves accuracy for identifying white blood cells (leukocytes).

Area of Science:

  • Medical Imaging Analysis
  • Computational Pathology
  • Biomedical Image Processing

Background:

  • Leukocyte segmentation in peripheral blood images is crucial for hematological analysis.
  • Existing methods struggle with uneven illumination and subtle leukocyte features, impacting accuracy.
  • Accurate segmentation is vital for automated blood cell counting and disease diagnosis.

Purpose of the Study:

  • To develop an improved leukocyte segmentation method addressing illumination and feature visibility issues.
  • To enhance the accuracy and reliability of automated white blood cell identification in digital images.
  • To introduce a novel image processing technique combining adaptive Retinex correction and U-net.

Main Methods:

  • Peripheral blood images are processed using adaptive Retinex correction based on MSRCR with Michelson contrast.

More Related Videos

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

2.9K
Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application
05:56

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

Published on: April 14, 2023

2.6K

Related Experiment Videos

Last Updated: Sep 4, 2025

Efficient and Consistent Generation of Retinal Pigment Epithelium/Choroid Flatmounts from Human Eyes for Histological Analysis
07:59

Efficient and Consistent Generation of Retinal Pigment Epithelium/Choroid Flatmounts from Human Eyes for Histological Analysis

Published on: October 28, 2022

2.9K
Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

2.9K
Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application
05:56

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

Published on: April 14, 2023

2.6K
  • The enhanced images are then segmented using the U-net convolutional neural network architecture.
  • The method focuses on redefining color recovery functions for clearer leukocyte differentiation.

    • Main Results:

    • Experimental evaluations on Cellavision, BCCD, and LISC datasets demonstrate superior performance.
    • The proposed method achieved a high segmentation accuracy rate of 98.87%.
    • Results indicate significant improvement over existing state-of-the-art leukocyte segmentation techniques.

    Conclusions:

    • The adaptive Retinex correction and U-net based method effectively overcomes challenges in leukocyte segmentation.
    • This approach offers a more accurate and robust solution for analyzing peripheral blood cell images.
    • The findings have implications for advancing automated hematology diagnostics and research.