Introduction

We present a novel solution to the problem of segmenting and splitting images of cells in an automatic and unsupervised manner [1] (SEG-SELF) and (RFOVE) [4].

Methodology of SEG-SELF [1]

• The detection of cell nuclei is based on the Bradley’s method [2].
• False positives are automatically identified and rejected based on shape and intensity features.
• The proposed method is able to automatically detect and split touching cells.
• To do so, we employ a variant of a region based multiellipse fitting method [3] that makes use of constraints on the area of the split cells.

Methodology of RFOVE [4]

Figure 3:(a) The green and blue points constitute the foreground. The figure shows the results of Decremental Ellipse Fitting Algorithm (DEFA) [3] when forced to estimate two ellipses. The two colors indicate the achieved assignment of points to ellipses. (b) DEFA result for the image of (a) when DEFA estimates a single ellipse. (c) The result from the application of the proposed RFOVE algorithm on the same data. The yellow points participate in the estimation on both ellipses. (d) The result of RFOVE in a synthetic image from [5]. (e) A fluorescence microscopy image and (f) the output of the proposed method. In (d) and (f) the boundaries of the detected objects are shown in green color and their ground truth centroids are plotted as red pluses.

• RFOVE is completely unsupervised, operates without any assumption or prior knowledge on the object’s shape and extends and improves the Decremental Ellipse Fitting Algorithm (DEFA) [3].
• Both RFOVE and DEFA solve the multi-ellipse fitting problem by performing model selection that is guided by the minimization of the Akaike Information Criterion on a suitably defined shape complexity measure.
• In contrast to DEFA, RFOVE minimizes an objective function that allows for ellipses with higher degree of overlap and, thus, achieves better ellipse-based shape approximation.