Improving concave point detection to better segment overlapped objects in images

TL;DR

This paper introduces an improved method for detecting concave points on object contours to enhance segmentation of overlapping objects in images, using curvature analysis and recursive refinement, validated on synthetic and real blood smear data.

Contribution

It presents a novel curvature-based concave point detection method that outperforms existing techniques in segmenting overlapped objects, demonstrated through synthetic and medical image experiments.

Findings

Better concave point detection leads to improved object segmentation.

Synthetic dataset effectively evaluates detection accuracy.

Application to blood cell images improves cluster separation.

Abstract

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.