InShaDe: Invariant Shape Descriptors for visual analysis of histology 2D cellular and nuclear shapes

We 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.

@InProceedings{AACBYPGS20,
  author       = {Agus, M. and Al-Thelaya, K. and Cali, C. and boido, m. and Yang, Y. and Pintore, G. and Gobbetti, E. and Schneider, J.},
  title        = {InShaDe: Invariant Shape Descriptors for visual analysis of histology 2D cellular and nuclear shapes},
  booktitle    = {Eurographics Workshop on Visual Computing for Biology and Medicine (VCBM)},
  pages        = {61--70},
  month        = {september},
  year         = {2020},
  keywords     = {shape processing, cellular nuclear envelopes, visual analysis, histology images},
  doi          = {10.2312/vcbm.20201173},
  url          = {https://publications.crs4.it/pubdocs/2020/AACBYPGS20},
}