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

Search research articles

Related Experiment Video

Updated: Jun 23, 2026

SCAnED - An Open-source Skin Segmentation Macro for Semi-automated Cell and Nuclei Detection in Epidermal and Dermal Skin Compartments

SCAnED - An Open-source Skin Segmentation Macro for Semi-automated Cell and Nuclei Detection in Epidermal and Dermal Skin Compartments

Published on: August 8, 2025

Extraction of specific parameters for skin tumour classification.

M Messadi¹, A Bessaid, A Taleb-Ahmed

¹Biomedical Engineering Laboratory, Department of Biomedical Electronics, Sciences Engineering Faculty, Abou Bekr Belkaid University, Tlemcen, Algeria. m_messadi@mail.univ-tlemcen.dz

Journal of Medical Engineering & Technology

|April 23, 2009

Summary

This study presents a computer-aided diagnosis method for classifying skin tumors using dermoscopy images. It extracts key features to differentiate melanoma from benign lesions, aiding early detection and improving patient outcomes.

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...

You might also read

Related Articles

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

Sort by

Same author

Knowledge-based tensor subspace analysis system for kinship verification.

Neural networks : the official journal of the International Neural Network Society·2022

Same author

Feature fusion via Deep Random Forest for facial age estimation.

Neural networks : the official journal of the International Neural Network Society·2020

Same author

Reducing the number of cameras and increasing the calibrated volume for motion capture.

Computer methods in biomechanics and biomedical engineering·2017

Same author

Context-specific selection of algorithms for recursive feature tracking in endoscopic image using a new methodology.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society·2014

Same author

Optimisation technique of camera placement for motion capture simulations.

Computer methods in biomechanics and biomedical engineering·2014

Same author

Combining WMN and FOCUSS recursive approach to estimating the current density distribution in the brain.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2007

Area of Science:

Dermatology
Medical Imaging
Computer-Aided Diagnosis

Background:

Melanoma is an aggressive skin cancer with increasing incidence.
Early detection of melanoma is crucial for reducing mortality rates.
Computer-aided diagnosis can assist general practitioners in identifying suspicious skin lesions.

Purpose of the Study:

To develop a methodological approach for classifying skin tumors in dermoscopy images.
To extract specific attributes for computer-aided diagnosis of melanoma.
To differentiate between melanoma and benign skin lesions.

Main Methods:

Preprocessing steps include hair removal and automatic image segmentation.
Segmentation refines lesion boundaries using image edges.

More Related Videos

On-Site Sampling and Extraction of Brain Tumors for Metabolomics and Lipidomics Analysis

On-Site Sampling and Extraction of Brain Tumors for Metabolomics and Lipidomics Analysis

Published on: May 31, 2020

Related Experiment Videos

Last Updated: Jun 23, 2026

SCAnED - An Open-source Skin Segmentation Macro for Semi-automated Cell and Nuclei Detection in Epidermal and Dermal Skin Compartments

SCAnED - An Open-source Skin Segmentation Macro for Semi-automated Cell and Nuclei Detection in Epidermal and Dermal Skin Compartments

Published on: August 8, 2025

On-Site Sampling and Extraction of Brain Tumors for Metabolomics and Lipidomics Analysis

On-Site Sampling and Extraction of Brain Tumors for Metabolomics and Lipidomics Analysis

Published on: May 31, 2020

Extraction of asymmetry, border, color, and diameter (ABCD) attributes.

An artificial neural network classifies lesions based on extracted ABCD features.

Main Results:

The developed method effectively extracts diagnostic attributes from dermoscopy images.
The ABCD features provide sufficient information to differentiate melanoma from benign lesions.
The approach facilitates computer-aided diagnosis for melanoma detection.

Conclusions:

The proposed methodology offers a robust approach for melanoma classification.
This technique can aid in the early detection of malignant skin tumors.
The computer-aided diagnosis system can support clinical decision-making for general practitioners.