Next Article in Journal
A Multi-Sensor Based Roadheader Positioning Model and Arbitrary Tunnel Cross Section Automatic Cutting
Next Article in Special Issue
MIGOU: A Low-Power Experimental Platform with Programmable Logic Resources and Software-Defined Radio Capabilities
Previous Article in Journal
Smooth Surface Visual Imaging Method for Eliminating High Reflection Disturbance
Previous Article in Special Issue
A Robust Localization System for Inspection Robots in Sewer Networks
Open AccessArticle

Automatic Focus Assessment on Dermoscopic Images Acquired with Smartphones

Fraunhofer Portugal AICOS, 4200-135 Porto, Portugal
Author to whom correspondence should be addressed.
Sensors 2019, 19(22), 4957;
Received: 30 September 2019 / Revised: 31 October 2019 / Accepted: 10 November 2019 / Published: 14 November 2019
(This article belongs to the Special Issue Mobile Sensing: Platforms, Technologies and Challenges)
Over recent years, there has been an increase in popularity of the acquisition of dermoscopic skin lesion images using mobile devices, more specifically using the smartphone camera. The demand for self-care and telemedicine solutions requires suitable methods to guide and evaluate the acquired images’ quality in order to improve the monitoring of skin lesions. In this work, a system for automated focus assessment of dermoscopic images was developed using a feature-based machine learning approach. The system was designed to guide the user throughout the acquisition process by means of a preview image validation approach that included artifact detection and focus validation, followed by the image quality assessment of the acquired picture. This paper also introduces two different datasets, dermoscopic skin lesions and artifacts, which were collected using different mobile devices to develop and test the system. The best model for automatic preview assessment attained an overall accuracy of 77.9% while focus assessment of the acquired picture reached a global accuracy of 86.2%. These findings were validated by implementing the proposed methodology within an android application, demonstrating promising results as well as the viability of the proposed solution in a real life scenario. View Full-Text
Keywords: mobile dermatology; image acquisition; image quality assessment; feature extraction; machine learning mobile dermatology; image acquisition; image quality assessment; feature extraction; machine learning
Show Figures

Figure 1

MDPI and ACS Style

Alves, J.; Moreira, D.; Alves, P.; Rosado, L.; Vasconcelos, M.J.M. Automatic Focus Assessment on Dermoscopic Images Acquired with Smartphones. Sensors 2019, 19, 4957.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

Back to TopTop