Abstract
We present RFOVE, a region-based method for approximating an arbitrary 2D shape with an automatically determined number of possibly overlapping ellipses. 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) [1]. 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. However, 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. A comparative evaluation of RFOVE with DEFA on several standard datasets shows that RFOVE achieves better shape coverage with simpler models (less ellipses). As a practical exploitation of RFOVE, we present its application to the problem of detecting and segmenting potentially overlapping cells in fluorescence microscopy images. Quantitative results obtained in three public datasets (one synthetic and two with more than 4000 actual stained cells) show the superiority of RFOVE over the state of the art in overlapping cells segmentation.
Published Version
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