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

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Density-based parallel skin lesion border detection with webCL.

James Lemon, Sinan Kockara, Tansel Halic

BMC Bioinformatics

|October 2, 2015

Summary

This summary is machine-generated.

A new parallel processing method using WebCL significantly speeds up skin lesion border detection in dermoscopy images without sacrificing accuracy. This technology aids dermatologists in early melanoma diagnosis by enabling faster analysis of high-resolution images directly from web browsers.

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Area of Science:

Medical Imaging
Computer Vision
Computational Science

Background:

Dermoscopy is crucial for diagnosing melanoma and pigmented skin lesions.
Accurate lesion border detection is vital for dermoscopic image analysis.
Current manual border delineation by dermatologists is subjective and prone to variations.

Purpose of the Study:

To develop and evaluate an automated, fast, and accurate method for skin lesion border detection.
To leverage parallel computing via WebCL for efficient dermoscopic image analysis.
To improve the accessibility and portability of advanced image analysis tools in clinical settings.

Main Methods:

Implemented a density-based clustering technique for skin lesion border detection.
Parallelized the algorithm using WebCL for execution in web browsers.
Utilized heterogeneous computing platforms (multi-core CPUs, GPUs) for parallel processing.

Main Results:

The parallel WebCL method achieved the same accuracy as the serial version.
Achieved a mean border error of 6.94%, recall of 76.66%, and precision of 99.29% on 100 dermoscopy images.
Demonstrated an average speedup factor of ~491.2 with the WebCL parallel version.

Conclusions:

Fast processing of dermoscopy images is essential for early melanoma detection.
WebCL enables efficient, parallel biomedical image processing directly from web browsers.
This parallelized approach can assist dermatologists in timely diagnosis, especially for large datasets.