An image based system implementing a well-known diagnostic method is disclosed for the automatic detection of melanomas as support to clinicians. The software architecture is able to receive and store digital ELM images captured by handheld dermoscopy and smartphones, recognize automatically the skin lesion within the digital image, measure morphological and chromatic parameters, carry out a suitable classification for detecting the dermoscopic structures included in the 7-Point Checklist and finally provide a second opinion supporting the dermatologist for the clinical decision. Advanced techniques are introduced at different stages of the image processing pipeline, including the border detection, the extraction of low-level features and scoring of high order features. Performance validation of the measurement system relies to the clinical practice by physicians with different experience.
A distributed measurement system for dermoscopic analysis of pigmented skin lesions / Di Leo, G.; Paolillo, A.; Pietrosanto, A.; Sommella, P.; Fabbrocini, G.; Cacciapuoti, S.. - 2015:(2015), pp. 1646-1651. (Intervento presentato al convegno 2015 IEEE International Instrumentation and Measurement Technology Conference, I2MTC 2015 tenutosi a Palazzo dei Congressi, Pisa nel 2015) [10.1109/I2MTC.2015.7151526].
A distributed measurement system for dermoscopic analysis of pigmented skin lesions
Fabbrocini, G.;Cacciapuoti, S.
2015
Abstract
An image based system implementing a well-known diagnostic method is disclosed for the automatic detection of melanomas as support to clinicians. The software architecture is able to receive and store digital ELM images captured by handheld dermoscopy and smartphones, recognize automatically the skin lesion within the digital image, measure morphological and chromatic parameters, carry out a suitable classification for detecting the dermoscopic structures included in the 7-Point Checklist and finally provide a second opinion supporting the dermatologist for the clinical decision. Advanced techniques are introduced at different stages of the image processing pipeline, including the border detection, the extraction of low-level features and scoring of high order features. Performance validation of the measurement system relies to the clinical practice by physicians with different experience.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
