Improving concave point detection to better segment overlapped objects in images
This work addresses the segmentation of overlapped objects, such as cells in medical images, for applications like disease diagnosis, but it is incremental as it builds on existing concave point detection methods.
The paper tackles the problem of segmenting overlapping objects in images by improving concave point detection, demonstrating that better detection leads to better cluster division, with evaluation on a synthetic dataset and a case study on sickle cell anaemia blood smears.
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.