Marcelo Cicconet

Mirror Symmetry Histograms for Capturing Geometric Properties in Images

We propose a data structure that captures global geometric properties in images: Histogram of Mirror Symmetry Coefficients. We compute such a coefficient for every pair of pixels, and group them in a 6-dimensional histogram. By marginalizing the HMSC in various ways, we develop algorithms for a range of applications: detection of nearly-circular cells, location of the main axis of reflection symmetry, detection of cell-division in movies of developing embryos, detection of worm-tips and indirect cell-counting via supervised classification. Our approach generalizes a series of histogram-related methods, and the proposed algorithms perform with state-of-the-art accuracy.

Label free cell-tracking and division detection based on 2D time-lapse images for lineage analysis of early embryo development

In this paper we report a database and a series of techniques related to the problem of tracking cells, and detecting their divisions, in time-lapse movies of mammalian embryos. Our contributions are (1) a method for counting embryos in a well, and cropping each individual embryo across frames, to create individual movies for cell tracking; (2) a semi-automated method for cell tracking that works up to the 8-cell stage, along with a software implementation available to the public (this software was used to build the reported database); (3) an algorithm for automatic tracking up to the 4-cell stage, based on histograms of mirror symmetry coefficients captured using wavelets; (4) a cell-tracking database containing 100 annotated examples of mammalian embryos up to the 8-cell stage; and (5) statistical analysis of various timing distributions obtained from those examples.

Complex-valued hough transforms for circles

This paper proposes the use of complex variables to represent votes in the Hough transform for circle detection. Replacing the positive numbers classically used in the parameter space of the Hough transforms by complex numbers allows cancellation effects when adding up the votes. Cancellation and the computation of shape likelihood via a complex numbers magnitude square lead to more robust solutions than the “classic” algorithms, as shown by computational experiments on synthetic and real datasets. We note a resemblance to methods used in quantum theory.

Ellipses from triangles

We present an ellipse finding and fitting algorithm that uses points and tangents, rather than just points, as the basic unit of information. These units are analyzed in a hierarchy: points with tangents are paired into triangles in the first layer and pairs of triangles in the second layer vote for ellipse centers. The remaining parameters are estimated via robust linear algebra: eigen-decomposition and iteratively reweighed least squares. Our method outperforms the state-of-the-art approach in synthetic images and microscopic images of cells.

A convolutional approach to reflection symmetry

We present a convolutional approach to reflection symmetry detection in 2D. Our model, built on the products of complex-valued wavelet convolutions, simplifies previous edge-based pairwise methods. Being parameter-centered, as opposed to feature-centered, it has certain computational advantages when the object sizes are known a priori, as demonstrated in an ellipse detection application. The method outperforms the best-performing algorithm on the CVPR 2013 Symmetry Detection Competition Database in the single-symmetry case. Code and a new database for 2D symmetry detection is available.

Shape statistics for cell division detection in time-lapse videos of early mouse embryo

We describe a statistical approach to the problem of estimating the times of cell-division cycles in time-lapse movies of early mouse embryos. Our method is based on the likelihoods for cells of certain radii ranges to be in each frame - without actually locating or counting the cells. Computing the likelihoods consists of a voting scheme where votes come form quadruples of points in a way similar to the first step of the Randomized Hough Transform for ellipse detection. To locate divisions, we search for points of abrupt change in the matrix of likelihoods (built for all frames), and pick the two optimal division points using a dynamic programming algorithm. Our results for the first and second cell division cycles differ less than two frames from the medians of the annotated times in a database of 100 annotated videos, and outperform two other recent methods in the same set.