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

Skin Cancer

Skin Cancer

Skin cancer is a type of cancer that occurs when there is an abnormal growth of skin cells, usually triggered by damage to the DNA within the skin cells. It is primarily caused by exposure to ultraviolet (UV) radiation from the sun or artificial sources like tanning beds. Skin cancer is the most common type of cancer worldwide, and its incidence continues to rise.
Basal Cell Carcinoma (BCC): BCC is the most common type of skin cancer, accounting for about 80% of cases. It typically develops in...

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.

You might also read

Related Articles

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

Sort by

Same author

Skin pattern analysis for lesion classification using local isotropy.

Skin research and technology : official journal of International Society for Bioengineering and the Skin (ISBS) [and] International Society for Digital Imaging of Skin (ISDIS) [and] International Society for Skin Imaging (ISSI)·2011

Same author

Combination of features from skin pattern and ABCD analysis for lesion classification.

Skin research and technology : official journal of International Society for Bioengineering and the Skin (ISBS) [and] International Society for Digital Imaging of Skin (ISDIS) [and] International Society for Skin Imaging (ISSI)·2007

Same author

Simulation and analysis of optical skin lesion images.

Skin research and technology : official journal of International Society for Bioengineering and the Skin (ISBS) [and] International Society for Digital Imaging of Skin (ISDIS) [and] International Society for Skin Imaging (ISSI)·2006

Same author

Enhancement of lesion classification using divergence, curl and curvature of skin pattern.

Skin research and technology : official journal of International Society for Bioengineering and the Skin (ISBS) [and] International Society for Digital Imaging of Skin (ISDIS) [and] International Society for Skin Imaging (ISSI)·2004

Same author

Analysis of skin line pattern for lesion classification.

Skin research and technology : official journal of International Society for Bioengineering and the Skin (ISBS) [and] International Society for Digital Imaging of Skin (ISDIS) [and] International Society for Skin Imaging (ISSI)·2003

Search research articles

Related Experiment Video

Updated: May 21, 2026

The Three-Dimensional Human Skin Reconstruct Model: a Tool to Study Normal Skin and Melanoma Progression

The Three-Dimensional Human Skin Reconstruct Model: a Tool to Study Normal Skin and Melanoma Progression

Published on: August 3, 2011

Lesion classification using 3D skin surface tilt orientation.

Zhishun She¹, P S Excell

¹Institute of Arts, Science & Technology, Glyndwr University, Wrexham, UK. z.she@glyndwr.ac.uk

Skin Research and Technology : Official Journal of International Society for Bioengineering and the Skin (ISBS) [And] International Society for Digital Imaging of Skin (ISDIS) [And] International Society for Skin Imaging (ISSI)

|June 8, 2012

Summary

This summary is machine-generated.

This study introduces a novel 3D surface tilt orientation parameter for skin cancer detection. This new feature significantly improves the accuracy of distinguishing malignant melanoma from benign lesions when combined with existing ABCD features.

More Related Videos

Quantitative Visualization and Detection of Skin Cancer Using Dynamic Thermal Imaging

Quantitative Visualization and Detection of Skin Cancer Using Dynamic Thermal Imaging

Published on: May 5, 2011

A 3D Organotypic Melanoma Spheroid Skin Model

A 3D Organotypic Melanoma Spheroid Skin Model

Published on: May 18, 2018

Related Experiment Videos

Last Updated: May 21, 2026

The Three-Dimensional Human Skin Reconstruct Model: a Tool to Study Normal Skin and Melanoma Progression

The Three-Dimensional Human Skin Reconstruct Model: a Tool to Study Normal Skin and Melanoma Progression

Published on: August 3, 2011

Quantitative Visualization and Detection of Skin Cancer Using Dynamic Thermal Imaging

Quantitative Visualization and Detection of Skin Cancer Using Dynamic Thermal Imaging

Published on: May 5, 2011

A 3D Organotypic Melanoma Spheroid Skin Model

A 3D Organotypic Melanoma Spheroid Skin Model

Published on: May 18, 2018

Area of Science:

Dermatology
Medical Imaging
Computer Vision

Background:

Current skin cancer diagnostics primarily use 2D imaging, missing crucial 3D information.
The ABCD features, common in 2D analysis, are limited by the skin's inherent 3D structure.

Purpose of the Study:

To develop new diagnostic features for skin cancer detection by incorporating 3D information.
To explore the utility of a novel 3D surface tilt orientation parameter.

Main Methods:

Extracted skin patterns from white light clinical images using high-pass filtering.
Estimated surface tilt orientations of skin and lesions using shape from texture techniques.
Developed a lesion classifier combining 3D tilt orientation with traditional ABCD features.

Main Results:

The 3D surface tilt orientation feature alone achieved an ROC curve area of 0.78.
Combining 3D tilt orientation with ABCD features via PCA resulted in an ROC curve area of 0.85, significantly outperforming 2D ABCD analysis (0.65).
The new 3D feature demonstrated a significant enhancement in classifying benign and malignant skin lesions.

Conclusions:

The surface tilt orientation parameter shows significant potential for improving skin lesion classifier accuracy.
Integrating 3D information with ABCD features offers a promising approach for differentiating malignant melanoma from benign lesions.