Melanoma detection using delaunay triangulation

A. Pennisi; D. D. Bloisi; D. Nardi; A. R. Giampetruzzi; C. Mondino; A. Facchiano

doi:10.1109/ICTAI.2015.117

Melanoma detection using delaunay triangulation

A. Pennisi, D. D. Bloisi, D. Nardi, A. R. Giampetruzzi, C. Mondino, A. Facchiano

Istituto Dermopatico dell'Immacolata , IDI

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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.

Original language	English
Title of host publication	Proceedings - International Conference on Tools with Artificial Intelligence, ICTAI
Publisher	IEEE Computer Society
Pages	791-798
Number of pages	8
Volume	2016-January
ISBN (Print)	9781509001637
DOIs	https://doi.org/10.1109/ICTAI.2015.117
Publication status	Published - Jan 4 2016
Event	27th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2015 - Vietri sul Mare, Salerno, Italy Duration: Nov 9 2015 → Nov 11 2015

Other

Other	27th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2015
Country/Territory	Italy
City	Vietri sul Mare, Salerno
Period	11/9/15 → 11/11/15

Keywords

Automatic segmentation
Border detection
Dermoscopy images
Melanoma detection

ASJC Scopus subject areas

Software
Artificial Intelligence
Computer Science Applications

Access to Document

10.1109/ICTAI.2015.117

Cite this

Pennisi, A, Bloisi, DD, Nardi, D, Giampetruzzi, AR, Mondino, C & Facchiano, A 2016, Melanoma detection using delaunay triangulation. in Proceedings - International Conference on Tools with Artificial Intelligence, ICTAI. vol. 2016-January, 7372213, IEEE Computer Society, pp. 791-798, 27th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2015, Vietri sul Mare, Salerno, Italy, 11/9/15. https://doi.org/10.1109/ICTAI.2015.117

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title = "Melanoma detection using delaunay triangulation",

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.",

keywords = "Automatic segmentation, Border detection, Dermoscopy images, Melanoma detection",

author = "A. Pennisi and Bloisi, {D. D.} and D. Nardi and Giampetruzzi, {A. R.} and C. Mondino and A. Facchiano",

year = "2016",

month = jan,

day = "4",

doi = "10.1109/ICTAI.2015.117",

language = "English",

isbn = "9781509001637",

volume = "2016-January",

pages = "791--798",

booktitle = "Proceedings - International Conference on Tools with Artificial Intelligence, ICTAI",

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AU - Pennisi, A.

AU - Bloisi, D. D.

AU - Nardi, D.

AU - Giampetruzzi, A. R.

AU - Mondino, C.

AU - Facchiano, A.

PY - 2016/1/4

Y1 - 2016/1/4

N2 - 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.

AB - 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.

KW - Automatic segmentation

KW - Border detection

KW - Dermoscopy images

KW - Melanoma detection

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DO - 10.1109/ICTAI.2015.117

M3 - Conference contribution

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VL - 2016-January

SP - 791

EP - 798

BT - Proceedings - International Conference on Tools with Artificial Intelligence, ICTAI

PB - IEEE Computer Society

T2 - 27th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2015

Y2 - 9 November 2015 through 11 November 2015

ER -

Melanoma detection using delaunay triangulation

Abstract

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Keywords

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