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.

我们提出了RFOVE，这是一种基于区域的方法，用于通过自动确定数量的可能重叠的椭圆近似任意2D形状。RFOVE完全不受监督，无需任何假设或先验知识就可以对物体的形状进行操作，并且可以扩展和改进减量椭圆拟合算法（DEFA）[1]。RFOVE和DEFA都通过在适当定义的形状复杂性度量上执行以最小化Akaike信息准则为指导的模型选择来解决多椭圆拟合问题。但是，与DEFA相比，RFOVE最小化了目标函数，该目标函数允许椭圆具有更高的重叠度，因此实现了更好的基于椭圆的形状近似。在几个标准数据集上对带有REFVE的RFOVE的比较评估表明，RFOVE通过更简单的模型（椭圆形更少）实现了更好的形状覆盖。作为对RFOVE的一种实际利用，我们将其应用于检测和分割荧光显微镜图像中可能重叠的细胞的问题。在三个公共数据集中获得的定量结果（一个合成的，两个带有超过4000个实际染色的细胞）显示了RFOVE在重叠细胞分割方面优于现有技术。