The detection of malignant lesions in dermoscopic images by using automatic diagnostic tools can help in reducing mortality from melanoma. In this paper, we describe a fully-automatic algorithm for skin lesion segmentation in dermoscopic images. The proposed approach is highly accurate when dealing with benign lesions, while the detection accuracy significantly decreases when melanoma images are segmented. This particular behavior lead us to consider geometrical and color features extracted from the output of our algorithm for classifying melanoma images, achieving promising results.
Melanoma detection using delaunay triangulation / Pennisi, Andrea; Bloisi, Domenico Daniele; Nardi, Daniele; Giampetruzzi, A. R.; Mondino, C.; Facchiano, A.. - STAMPA. - (2016), pp. 791-798. (Intervento presentato al convegno 27th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2015; Vietri sul Mare, Salerno; Italy; 9 November 2015 through 11 November 2015 tenutosi a Vietri sul mare, Italy) [10.1109/ICTAI.2015.117].
Melanoma detection using delaunay triangulation
PENNISI, ANDREA
;BLOISI, Domenico Daniele
;NARDI, Daniele
;
2016
Abstract
The detection of malignant lesions in dermoscopic images by using automatic diagnostic tools can help in reducing mortality from melanoma. In this paper, we describe a fully-automatic algorithm for skin lesion segmentation in dermoscopic images. The proposed approach is highly accurate when dealing with benign lesions, while the detection accuracy significantly decreases when melanoma images are segmented. This particular behavior lead us to consider geometrical and color features extracted from the output of our algorithm for classifying melanoma images, achieving promising results.| File | Dimensione | Formato | |
|---|---|---|---|
|
Pennisi_Melanoma_2016.pdf
solo gestori archivio
Tipologia:
Versione editoriale (versione pubblicata con il layout dell'editore)
Licenza:
Tutti i diritti riservati (All rights reserved)
Dimensione
284.45 kB
Formato
Adobe PDF
|
284.45 kB | Adobe PDF | Contatta l'autore |
|
Pennisi_Postprint-Melanoma_2016.compressed.pdf
Open Access dal 01/08/2017
Tipologia:
Documento in Post-print (versione successiva alla peer review e accettata per la pubblicazione)
Licenza:
Tutti i diritti riservati (All rights reserved)
Dimensione
262.66 kB
Formato
Adobe PDF
|
262.66 kB | Adobe PDF | |
|
ICTAI2015_Frontespizio-indice_2016.pdf
solo gestori archivio
Tipologia:
Versione editoriale (versione pubblicata con il layout dell'editore)
Licenza:
Tutti i diritti riservati (All rights reserved)
Dimensione
160 kB
Formato
Adobe PDF
|
160 kB | Adobe PDF | Contatta l'autore |
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
