Identity statement area
Reference TypeConference Paper (Conference Proceedings)
Last Update2010:
Metadata Last Update2010:
Citation KeyParolinHerzJung:2010:SeDiMe
TitleSemi-Automated Diagnosis of Melanoma Through the Analysis of Dermatological Images
FormatPrinted, On-line.
Access Date2021, Jan. 25
Number of Files1
Size256 KiB
Context area
Author1 Parolin, Alessandro
2 Herzer, Eduardo
3 Jung, Claudio R.
Affiliation1 Unisinos
2 Unisinos
EditorBellon, Olga
Esperanša, Claudio
Conference NameConference on Graphics, Patterns and Images, 23 (SIBGRAPI)
Conference LocationGramado
DateAug. 30 - Sep. 3, 2010
Book TitleProceedings
PublisherIEEE Computer Society
Publisher CityLos Alamitos
Tertiary TypeFull Paper
History2010-10-01 04:19:38 :: -> :: 2010
Content and structure area
Is the master or a copy?is the master
Content Stagecompleted
Content TypeExternal Contribution
Keywordsimage processing, medical imaging, classification, MDA-FKT.
AbstractMelanoma is the deadliest kind of skin cancer, but it can be 100% cured if recognized early in advance. This paper proposes a non-invasive automated skin lesion classifier based on digitized dermatological images. In the proposed approach, the lesion is initially segmented using snakes guided by an edge map based on the Wavelet Transform (WT) computed at different resolutions. A set of features is extracted from lesion pixels, and a probabilistic classifier is used to identify melanoma lesions. The detection rate of the proposed system can be adjusted to control the tradeoff between false positives and false negatives, and experimental results indicated that a false negative rate of 1.89% can be achieved, in a total accuracy rate of 82.55%.
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