A review of the quantification and classification of pigmented skin lesions: from dedicated to hand-held devices

• Type: Article in International Scientific Journal
• Date: 2015

Authors

Mercedes Filho (Author) FEUP; Zhen Ma (Author) FEUP; João Manuel R. S. Tavares (Author) FEUP

Journal

Title: Journal of Medical Systems

Vol. 39 No. 177

ISSN: 0148-5598

Publisher: Springer Nature

Indexing

ISI Web of Knowledge - 70 Citations

ISI Web of Science

Scopus - 76 Citations

INSPEC

Scientific classification

FOS: Engineering and technology

CORDIS: Technological sciences

Other information

Authenticus ID: P-00G-P5P

DOI: 10.1007/s10916-015-0354-8

Abstract (EN): In recent years, the incidence of skin cancer cases has risen, worldwide, mainly due to the prolonged exposure to harmful ultraviolet radiation. Concurrently, the computerassisted medical diagnosis of skin cancer has undergone major advances, through an improvement in the instrument and detection technology, and the development of algorithms to process the information. Moreover, because there has been an increased need to store medical data, for monitoring, comparative and assisted-learning purposes, algorithms for data processing and storage have also become more efficient in handling the increase of data. In addition, the potential use of common mobile devices to register high-resolution images of skin lesions has also fueled the need to create real-time processing algorithms that may provide a likelihood for the development of malignancy. This last possibility allows even non-specialists to monitor and follow-up suspected skin cancer cases. In this review, we present the major steps in the preprocessing, processing and post-processing of skin lesion images, with a particular emphasis on the quantification and classification of pigmented skin lesions. We further review and outline the future challenges for the creation of minimum-feature, automated and real-time algorithms for the detection of skin cancer from images acquired via common mobile devices.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 12