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Proceedings Paper

Mean shift detection using active learning in dermatological images
Author(s): Gabriela Maletti; Bjarne Kjaer Ersboll; Knut Conradsen
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Paper Abstract

A scheme for detecting heterogenous regions in dermatological images with malignant melanoma is proposed. The scheme works without setting any parameter. The mean shift detection problem is divided into two stages: window size optimization and detection. In the first stage, the maximum circular neighborhood centered on each pixel for which it is true that all the elements belong to the same class as the central one is estimated using redundant data sets generated with overlapping groups. Statistics are computed from all these neighborhoods and associated ot the respective central pixels. As expected, larger values of a minimizing energy function are assigned to pixels belonging to heterogeneous regions. In the second stage, those regions are detected by applying first an expectation-maximization algorithm and, afterwards, automatically defining a threshold between homogeneous and heterogeneous regions. The present scheme is tested on a set of synthetical images. Results are shown on synthetical and real images. Extensions of the scheme to textural cases are also shown.

Paper Details

Date Published: 9 May 2002
PDF: 8 pages
Proc. SPIE 4684, Medical Imaging 2002: Image Processing, (9 May 2002); doi: 10.1117/12.467152
Show Author Affiliations
Gabriela Maletti, Technical Univ. of Denmark (Denmark)
Bjarne Kjaer Ersboll, Technical Univ. of Denmark (Denmark)
Knut Conradsen, Technical Univ. of Denmark (Denmark)


Published in SPIE Proceedings Vol. 4684:
Medical Imaging 2002: Image Processing
Milan Sonka; J. Michael Fitzpatrick, Editor(s)

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