Towards robust identification and tracking of nevi in sparse photographic time series

Jakob Vogel; Alexandru Duliu; Yuji Oyamada; Jose Gardiazabal; Tobias Lasser; Mahzad Ziai; Rüdiger Hein; Nassir Navab

doi:10.1117/12.2043788

20 March 2014 Towards robust identification and tracking of nevi in sparse photographic time series

Jakob Vogel, Alexandru Duliu, Yuji Oyamada, Jose Gardiazabal, Tobias Lasser, Mahzad Ziai, Rüdiger Hein, Nassir Navab

Proceedings Volume 9035, Medical Imaging 2014: Computer-Aided Diagnosis; 90353D (2014) https://doi.org/10.1117/12.2043788
Event: SPIE Medical Imaging, 2014, San Diego, California, United States

Abstract

In dermatology, photographic imagery is acquired in large volumes in order to monitor the progress of diseases, especially melanocytic skin cancers. For this purpose, overview (macro) images are taken of the region of interest and used as a reference map to re-localize highly magni ed images of individual lesions. The latter are then used for diagnosis. These pictures are acquired at irregular intervals under only partially constrained circumstances, where patient positions as well as camera positions are not reliable. In the presence of a large number of nevi, correct identi cation of the same nevus in a series of such images is thus a time consuming task with ample chances for error. This paper introduces a method for largely automatic and simultaneous identi cation of nevi in di erent images, thus allowing the tracking of a single nevus over time, as well as pattern evaluation. The method uses a rotation-invariant feature descriptor that uses the local neighborhood of a nevus to describe it. The texture, size and shape of the nevus are not used to describe it, as these can change over time, especially in the case of a malignancy. We then use the Random Walks framework to compute the correspondences based on the probabilities derived from comparing the feature vectors. Evaluation is performed on synthetic and patient data at the university clinic.

Citation Download Citation

Jakob Vogel, Alexandru Duliu, Yuji Oyamada, Jose Gardiazabal, Tobias Lasser, Mahzad Ziai, Rüdiger Hein, and Nassir Navab "Towards robust identification and tracking of nevi in sparse photographic time series", Proc. SPIE 9035, Medical Imaging 2014: Computer-Aided Diagnosis, 90353D (20 March 2014); https://doi.org/10.1117/12.2043788

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