Jove
Visualize
Contact Us

Related Concept Videos

Classification of Leukocytes01:30

Classification of Leukocytes

6.6K
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...
6.6K
Lymphoid Cells and Tissues01:18

Lymphoid Cells and Tissues

3.5K
Lymphoid cells and tissues are integral to the immune system, which is crucial in maintaining our body's defense against harmful pathogens. They form the building blocks of lymphoid organs, which include the spleen, thymus, and lymph nodes.
Lymphoid cells consist of various types of immune system cells. These include B and T lymphocytes, which are responsible for producing antibodies and killing infected cells, respectively. Dendritic cells act as messengers between the innate and adaptive...
3.5K

You might also read

Related Articles

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

Sort by
Same author

Seasonal and geographic variations in vitamin D levels among patients with familial Mediterranean fever: a systematic review and meta-analysis.

Clinical rheumatology·2026
Same author

Improving heating uniformity of pathological tissue specimens inside a domestic microwave oven.

The Journal of microwave power and electromagnetic energy : a publication of the International Microwave Power Institute·2014
Same author

Three dimensional approach for realistic simulation of facial soft tissue response: a pilot study.

Progress in orthodontics·2011
See all related articles
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: Mar 2, 2026

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
07:13

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Published on: April 18, 2025

828

Decision Support System for Lymphoma Classification.

Ahmed E-S Negm1, Ahmed H Kandil1,2, Osama A E-F Hassan1,3

  • 1Systems and Biomedical Engineering Department, High Institutes of Engineering, Al Shorouk Academy, Al Shorouk city, Cairo, Egypt.

Current Medical Imaging Reviews
|May 12, 2017
PubMed
Summary
This summary is machine-generated.

This study introduces an automated system to classify diffuse lymphoma cells using image analysis of morphological features like size and circularity. This digital approach aids pathologists in diagnosis and medical training.

Keywords:
Decision Support SystemDiffuse lymphomaImage processingMorphology

More Related Videos

Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning
08:58

Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning

Published on: November 19, 2018

13.2K
Multiplexed Fluorescent Immunohistochemical Staining, Imaging, and Analysis in Histological Samples of Lymphoma
07:52

Multiplexed Fluorescent Immunohistochemical Staining, Imaging, and Analysis in Histological Samples of Lymphoma

Published on: January 9, 2019

20.6K

Related Experiment Videos

Last Updated: Mar 2, 2026

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
07:13

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Published on: April 18, 2025

828
Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning
08:58

Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning

Published on: November 19, 2018

13.2K
Multiplexed Fluorescent Immunohistochemical Staining, Imaging, and Analysis in Histological Samples of Lymphoma
07:52

Multiplexed Fluorescent Immunohistochemical Staining, Imaging, and Analysis in Histological Samples of Lymphoma

Published on: January 9, 2019

20.6K

Area of Science:

  • Oncology
  • Pathology
  • Medical Imaging

Background:

  • Diffuse lymphoma is a malignant lymphoid tissue tumor characterized by uncontrolled cell proliferation.
  • Manual identification of diffuse lymphoma cells by pathologists is time-consuming and subjective.
  • Accurate classification is crucial for effective treatment and prognosis.

Purpose of the Study:

  • To develop an automated system for classifying diffuse lymphoma cell categories.
  • To utilize morphological features for objective and efficient cell differentiation.
  • To provide a user-friendly tool for pathologists and medical education.

Main Methods:

  • Image processing techniques applied to digital microscopic images.
  • Analysis of morphological cell features: size, perimeter, circularity, area, and color density.
  • Consideration of cell count and addressing image artifacts like overlapping and distortion.

Main Results:

  • Successful identification and classification of lymphoid cell populations in digital images.
  • Quantification of morphological parameters to overcome image quality issues.
  • Development of an automated system to support pathological decision-making.

Conclusions:

  • The automated system offers a reliable method for diffuse lymphoma cell classification.
  • This technology can enhance diagnostic accuracy and consistency.
  • The system serves as a valuable tool for training medical professionals in lymphoma diagnosis.