Agus, MarcoAl-Thelaya, KhaledCali, CorradoBoido, Marina M.Yang, YinPintore, GiovanniGobbetti, EnricoSchneider, JensKozlíková, Barbora and Krone, Michael and Smit, Noeska and Nieselt, Kay and Raidou, Renata Georgia2020-09-282020-09-282020978-3-03868-109-02070-5786https://doi.org/10.2312/vcbm.20201173https://diglib.eg.org:443/handle/10.2312/vcbm20201173We present a shape processing framework for visual exploration of cellular nuclear envelopes extracted from histology images. The framework is based on a novel shape descriptor of closed contours relying on a geodesically uniform resampling of discrete curves to allow for discrete differential-geometry-based computation of unsigned curvature at vertices and edges. Our descriptor is, by design, invariant under translation, rotation and parameterization. Moreover, it additionally offers the option for uniform-scale-invariance. The optional scale-invariance is achieved by scaling features to z-scores, while invariance under parameterization shifts is achieved by using elliptic Fourier analysis (EFA) on the resulting curvature vectors. These invariant shape descriptors provide an embedding into a fixed-dimensional feature space that can be utilized for various applications: (i) as input features for deep and shallow learning techniques; (ii) as input for dimension reduction schemes for providing a visual reference for clustering collection of shapes. The capabilities of the proposed framework are demonstrated in the context of visual analysis and unsupervised classification of histology images.Applied computingImagingComputing methodologiesShape representationsCluster analysis10.2312/vcbm.2020117361-70