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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...

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Related Experiment Video

Updated: Jun 4, 2026

Combining Reflectance Confocal Microscopy with Optical Coherence Tomography for Noninvasive Diagnosis of Skin Cancers via Image Acquisition

Combining Reflectance Confocal Microscopy with Optical Coherence Tomography for Noninvasive Diagnosis of Skin Cancers via Image Acquisition

Published on: August 18, 2022

Automatic skin tumour border detection for digital dermoscopy using a new digital image analysis scheme.

Q Abbas¹, I F García, M Rashid

¹Department of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, PR China. qaisarabbasphd@gmail.com

British Journal of Biomedical Science

|February 8, 2011

Summary

This study presents an automated method for precise skin lesion border detection in dermoscopy images. The approach enhances early melanoma detection by improving segmentation accuracy and reducing errors.

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Dermoscopy Aids in the Diagnosis of Discoid Lupus Erythematosus

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Published on: May 16, 2025

Area of Science:

Dermatology
Medical Image Analysis
Computer Vision

Background:

Accurate skin lesion classification is crucial for diagnosing conditions like malignant melanoma and basal cell carcinoma.
Challenges in border detection include illumination variations, dermoscopic gel, and inherent skin features (blood vessels, hair, skin lines).

Purpose of the Study:

To develop an automated method for enhanced sensitivity and specificity in detecting multiple skin lesion borders from dermoscopy images.
To minimize the impact of interfering features on border detection for improved early melanoma and pigment lesion identification.

Main Methods:

An automated border detection technique utilizing geodesic active contour energy minimization.
Integration of homomorphic, median, and anisotropic diffusion (AD) filtering, along with top-hat watershed transformation for noise reduction and feature enhancement.

Main Results:

The proposed method significantly improved border detection accuracy in real dermoscopic images.
Segmentation error rates were reduced from 12.42% to 7.23% through the integrated enhancement and noise removal algorithm.

Conclusions:

The validated integrated approach enhances lesion border detection and noise removal, potentially improving skin cancer classification.
This automated method offers a promising tool for more accurate and sensitive analysis of dermoscopic images.