Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Classification of Connective Tissues

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: Overview

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 Epithelium

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...

Classification of Epithelial Tissues: Simple Epithelium

Classification of Epithelial Tissues: Simple Epithelium

Simple epithelium consists of a single layer of cells that lines body cavities and blood vessels. The shape of the cells in the epithelium reflects the function of the tissue. Cells in simple squamous epithelium appear as thin scales with flat, elliptical nuclei that mirror the form of the cell.
Because of the thinness of the cells, simple squamous epithelium is present where the rapid passage of chemical compounds is observed. For example, the endothelium that lines the capillaries and vessels...

Tissues

Tissues

Tissues are a group of cells that share a common embryonic origin. Microscopic observation reveals that the cells in a tissue share morphological features and are arranged in an orderly pattern to perform specific functions. From an evolutionary perspective, tissues appear in more complex organisms. Although there are many types of cells in the human body, they are organized into four broad categories of tissues: epithelial, connective, muscle, and nervous. Each of these categories is...

Tissues

Tissues

Cells with similar structure and function are grouped into tissues. A group of tissues with a specialized function is called an organ. There are four main types of tissue in vertebrates: epithelial, connective, muscle, and nervous.

You might also read

Related Articles

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

Sort by

Same author

Representation learning approach for understanding structured documents.

Scientific reports·2025

Same author

Silicodata: An Annotated Benchmark CXR Dataset for Silicosis Detection.

Scientific data·2025

Same author

MRI detection and grading of knee osteoarthritis - a pilot study using an AI technique with a novel imaging-based scoring system.

Biomaterials science·2025

Same author

A decentralized privacy-preserving XR system for 3D medical data visualization using hybrid biometric cryptosystem.

Scientific reports·2025

Same author

Differential Pattern of Brain Metabolism in Drug-naive versus Refractory OCD using [18F]-FDG PET/MRI Brain.

Indian journal of nuclear medicine : IJNM : the official journal of the Society of Nuclear Medicine, India·2025

Same author

K<sup>trans</sup> Calculation Using Reference Method Corrected Native T<sub>10</sub> for Breast Cancer Diagnosis.

Journal of medical physics·2023

Search research articles

Related Experiment Video

Updated: Jul 5, 2026

Visualization, Quantification, and Mapping of Immune Cell Populations in the Tumor Microenvironment

Visualization, Quantification, and Mapping of Immune Cell Populations in the Tumor Microenvironment

Published on: March 25, 2020

Handcrafted fuzzy rules for tissue classification.

Shashi Bhushan Mehta¹, Santanu Chaudhury, Asok Bhattacharyya

¹Philips Innovation Campus, Nagavara, Bangalore 560045, India. sbm20@yahoo.com

Magnetic Resonance Imaging

|May 16, 2008

Summary

A new fuzzy rule-based system accurately segments brain tissues in magnetic resonance (MR) images. This method effectively identifies white matter, gray matter, and cerebrospinal fluid, showing good agreement with expert manual segmentation.

More Related Videos

Morphology-Based Distinction Between Healthy and Pathological Cells Utilizing Fourier Transforms and Self-Organizing Maps

Morphology-Based Distinction Between Healthy and Pathological Cells Utilizing Fourier Transforms and Self-Organizing Maps

Published on: October 28, 2018

Reconstruction of 3-Dimensional Histology Volume and its Application to Study Mouse Mammary Glands

Reconstruction of 3-Dimensional Histology Volume and its Application to Study Mouse Mammary Glands

Published on: July 26, 2014

Related Experiment Videos

Last Updated: Jul 5, 2026

Visualization, Quantification, and Mapping of Immune Cell Populations in the Tumor Microenvironment

Visualization, Quantification, and Mapping of Immune Cell Populations in the Tumor Microenvironment

Published on: March 25, 2020

Morphology-Based Distinction Between Healthy and Pathological Cells Utilizing Fourier Transforms and Self-Organizing Maps

Morphology-Based Distinction Between Healthy and Pathological Cells Utilizing Fourier Transforms and Self-Organizing Maps

Published on: October 28, 2018

Reconstruction of 3-Dimensional Histology Volume and its Application to Study Mouse Mammary Glands

Reconstruction of 3-Dimensional Histology Volume and its Application to Study Mouse Mammary Glands

Published on: July 26, 2014

Area of Science:

Medical Imaging
Artificial Intelligence
Biomedical Engineering

Background:

Magnetic Resonance (MR) imaging is crucial for brain tissue analysis.
Segmentation of brain tissues (white matter, gray matter, cerebrospinal fluid) is essential for diagnosis and research.
Gradual intensity variations at tissue boundaries in MR images pose segmentation challenges.

Purpose of the Study:

To propose a handcrafted fuzzy rule-based system for improved brain tissue segmentation in MR images.
To address the challenge of gradual intensity variations at tissue boundaries.
To accurately identify and classify white matter, gray matter, and cerebrospinal fluid.

Main Methods:

Development of a fuzzy rule-based system utilizing histogram and spatial neighborhood features.
Application of the system to T2-weighted axial MR brain images.
Classification of image pixels into white matter, gray matter, and cerebrospinal fluid.

Main Results:

The proposed fuzzy system demonstrated effective segmentation of brain tissues.
The system successfully classified pixels into the three primary tissue types.
Results showed good agreement when compared to manual segmentation by an expert.

Conclusions:

A handcrafted fuzzy rule-based system offers a robust approach for MR brain image segmentation.
The system's ability to handle intensity variations makes it suitable for MR physics.
The findings support the utility of fuzzy logic in medical image analysis and tissue classification.