9 May 2002 Mean shift detection using active learning in dermatological images
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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.
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Gabriela Maletti, Gabriela Maletti, Bjarne Kjaer Ersboll, Bjarne Kjaer Ersboll, Knut Conradsen, Knut Conradsen, } "Mean shift detection using active learning in dermatological images", Proc. SPIE 4684, Medical Imaging 2002: Image Processing, (9 May 2002); doi: 10.1117/12.467152; https://doi.org/10.1117/12.467152

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