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Related Concept Videos

Classification of Connective Tissues01:30

Classification of Connective Tissues

The connective tissues have different properties and functions in the human body. They are broadly categorized into proper, supporting, or fluid connective tissues.
Connective Tissue Proper
Connective tissue proper is the most abundant class of connective tissues. As its name implies, it predominantly connects different tissues in the body. Depending on the cell types, ground substance, viscosity, and fiber types in the ECM, connective tissue proper is further categorized into loose and dense.
Classification of Epithelial Tissues: Overview01:22

Classification of Epithelial Tissues: Overview

Epithelial tissues are classified according to the shape of the cells and the number of cell layers formed. Cell shapes can be squamous (flattened and thin), cuboidal (square-like, as wide as it is tall), or columnar (rectangular, taller than it is wide). Additionally, the nucleus shape helps identify the type of epithelial cells. Squamous cells have flattened disc-shaped nuclei, cuboidal cells have spherical nuclei, and columnar cells have elongated nuclei.
Based on the number of cell layers,...
Classification of Epithelial Tissues: Stratified Epithelium01:29

Classification of Epithelial Tissues: Stratified Epithelium

Stratified epithelium consists of several stacked layers of cells. They provide the durability to withstand constant physical and chemical attacks. Stratified epithelium is named after the shape of the most apical layer of cells. Stratified squamous epithelium is the most common type found in the human body. In this tissue, the apical cells are squamous, whereas the basal layer contains either columnar or cuboidal cells. The basal cells divide to form new daughter cells, which gradually become...

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Related Experiment Video

Updated: May 29, 2026

Quantitation of Protein Expression and Co-localization Using Multiplexed Immuno-histochemical Staining and Multispectral Imaging
08:40

Quantitation of Protein Expression and Co-localization Using Multiplexed Immuno-histochemical Staining and Multispectral Imaging

Published on: April 8, 2016

Automatic colposcopy video tissue classification using higher order entropy-based image registration.

Juan D García-Arteaga1, Jan Kybic, Wenjing Li

  • 1Center for Machine Perception, Czech Technical University, Department of Cybernetics, Faculty of Electrical Engineering, Prague, Czech Republic. garcia@fel.cvut.cz

Computers in Biology and Medicine
|September 6, 2011
PubMed
Summary
This summary is machine-generated.

This study introduces a new method to quantify cervical tissue changes during colposcopy, improving early detection of cervical cancer. By using elastic image registration and temporal feature analysis, it enhances diagnostic accuracy and reproducibility.

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Area of Science:

  • Medical imaging
  • Gynecologic oncology
  • Computational pathology

Background:

  • Colposcopy is crucial for early detection of cervical intraepithelial lesions and cancer.
  • Visual assessment during colposcopy can be subjective, impacting diagnostic reproducibility.
  • Acetic acid application causes transient acetowhite changes, a key diagnostic indicator.

Purpose of the Study:

  • To quantitatively analyze acetowhite decay dynamics during colposcopy.
  • To develop image analysis tools for objective and reproducible cervical cancer diagnosis.
  • To overcome subjectivity in colposcopy by quantifying tissue changes.

Main Methods:

  • Elastic image registration using a multi-feature entropy similarity criterion to align sequential colposcopic images.
  • Extraction of temporal features based on color properties and a dual compartment cervix model.
  • Pixel-wise classification using extracted temporal features.

Main Results:

  • Robust compensation for patient, camera, and tissue movement in cervical images.
  • Quantitative analysis of acetowhite decay levels across image sequences.
  • Demonstration of pixel-wise classification accuracy against histopathological ground truth.

Conclusions:

  • The proposed image registration and feature extraction method enables dense quantification of acetowhite decay.
  • This approach offers a promising tool to enhance objectivity and reproducibility in colposcopy.
  • Quantitative analysis can aid physicians in earlier and more accurate diagnosis of cervical abnormalities.