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) · 2020
Segmentation methods for acne vulgaris images: Proposal of a new methodology applied to fluorescence images.
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Peris Fajarnés Guillermo, Moncho Santonja María, Defez García Beatriz, Lengua Lengua Ismael
Аннотация
BACKGROUND: Acne vulgaris is one of the most common human pathologies worldwide. Its prevalence causes a high healthcare expenditure. Acne healthcare costs and effects on individuals' quality of life lead to the need of analysing current acne evaluation, treatment and monitoring methods. One of the most common ones is manual lesion counting by a dermatologist. However, this technique has several limitations, such as time spent. That is the reason why the development of new computer-assisted techniques is needed in order to automatically count the acne lesions.
MATERIALS AND METHODS: Using the fluorescence images, a segmentation algorithm is implemented in MATLAB.
RESULTS: A new counting tool has been obtained that provides a form of objective evaluation of acne vulgaris disease. The effectiveness of the application of the segmentation method is more than 90%, being valid for the follow-up and diagnosis of injuries.
CONCLUSION: Automated counting of acne lesions has been proposed to solve current limitations of evaluation and monitoring methods for acne vulgaris. It is clear that the use of machine learning algorithms such as k-means enables clinicians to objectively and quickly evaluate the severity of acne.
MATERIALS AND METHODS: Using the fluorescence images, a segmentation algorithm is implemented in MATLAB.
RESULTS: A new counting tool has been obtained that provides a form of objective evaluation of acne vulgaris disease. The effectiveness of the application of the segmentation method is more than 90%, being valid for the follow-up and diagnosis of injuries.
CONCLUSION: Automated counting of acne lesions has been proposed to solve current limitations of evaluation and monitoring methods for acne vulgaris. It is clear that the use of machine learning algorithms such as k-means enables clinicians to objectively and quickly evaluate the severity of acne.