Intelligent segmentation and classification of pigmented skin lesions in dermatological images

Maglogiannis, I; Zafiropoulos, E; Kyranoudis, C

dc.contributor.author	Maglogiannis, I	en
dc.contributor.author	Zafiropoulos, E	en
dc.contributor.author	Kyranoudis, C	en
dc.date.accessioned	2014-03-01T02:44:04Z
dc.date.available	2014-03-01T02:44:04Z
dc.date.issued	2006	en
dc.identifier.issn	0302-9743	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/31657
dc.subject	Computer Vision	en
dc.subject	Digital Image	en
dc.subject	Early Detection	en
dc.subject	Malignant Melanoma	en
dc.subject	Skin Cancer	en
dc.subject	Skin Lesion	en
dc.subject	Support Vector Machine	en
dc.subject.classification	Computer Science, Theory & Methods	en
dc.subject.other	Computer vision	en
dc.subject.other	Intelligent control	en
dc.subject.other	Learning systems	en
dc.subject.other	Medical imaging	en
dc.subject.other	Skin	en
dc.subject.other	Tumors	en
dc.subject.other	Dermatology clinics	en
dc.subject.other	Diagnostic systems	en
dc.subject.other	Intelligent segmentation	en
dc.subject.other	Malignant melanoma tumor	en
dc.subject.other	Image segmentation	en
dc.title	Intelligent segmentation and classification of pigmented skin lesions in dermatological images	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1007/11752912_23	en
heal.identifier.secondary	http://dx.doi.org/10.1007/11752912_23	en
heal.language	English	en
heal.publicationDate	2006	en
heal.abstract	During the last years, computer vision-based diagnostic systems have been used in several hospitals and dermatology clinics, aiming mostly at the early detection of malignant melanoma tumor, which is among the most frequent types of skin cancer, versus other types of non-malignant cutaneous diseases. In this paper we discuss intelligent techniques for the segmentation and classification of pigmented skin lesions in such dermatological images. A local thresholding algorithm is proposed for skin lesion separation and border, texture and color based features, are then extracted from the digital images. Extracted features are used to construct a classification module based on Support Vector Machines (SVM) for the recognition of malignant melanoma versus dysplastic nevus. © Springer-Verlag Berlin Heidelberg 2006.	en
heal.publisher	SPRINGER-VERLAG BERLIN	en
heal.journalName	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)	en
heal.bookName	LECTURE NOTES IN COMPUTER SCIENCE	en
dc.identifier.doi	10.1007/11752912_23	en
dc.identifier.isi	ISI:000238053100021	en
dc.identifier.volume	3955 LNAI	en
dc.identifier.spage	214	en
dc.identifier.epage	223	en