This paper presents a method that improve state-of-the-art of the concave point detection methods as a first step to segment overlapping objects on images. It is based on the analysis of the curvature of the objects contour. The method has three main steps. First, we pre-process the original image to obtain the value of the curvature on each contour point. Second, we select regions with higher curvature and we apply a recursive algorithm to refine the previous selected regions. Finally, we obtain a concave point from each region based on the analysis of the relative position of their neighbourhood We experimentally demonstrated that a better concave points detection implies a better cluster division. In order to evaluate the quality of the concave point detection algorithm, we constructed a synthetic dataset to simulate overlapping objects, providing the position of the concave points as a ground truth. As a case study, the performance of a well-known application is evaluated, such as the splitting of overlapped cells in images of peripheral blood smears samples of patients with sickle cell anaemia. We used the proposed method to detect the concave points in clusters of cells and then we separate this clusters by ellipse fitting.
翻译:本文展示了一种方法,作为将图像上的相重叠对象分隔开来的第一步,改进曲线点探测方法的先进水平,作为将图像上的相重叠对象分隔开来的第一步。它基于对对象轮廓轮廓的剖面分析。 方法有三个主要步骤。 首先, 我们预处理原始图像, 以获得每个轮廓点的曲线值。 第二, 我们选择曲线较高的区域, 并应用一种循环算法来改进先前选定的区域。 最后, 我们根据对每个区域相邻地区相对位置的分析, 从每个区域获得一个相交点。 我们实验性地证明, 更好的凝结点探测意味着一个更好的集群分解。 为了评估曲线点探测算法的质量, 我们设计了一个合成数据集, 模拟相重叠对象, 提供曲线点的位置作为地面真相。 作为案例研究, 我们评估了已知应用程序的性能, 比如, 将相重叠的细胞细胞样本中的相重叠细胞分解, 我们用建议的方法来检测这些细胞分组的相配合点。