Abstract:
This paper presents a method for object detection in microscopy images, focusing on particle detection. The main objective of the research is to develop an algorithm capable of efficiently detecting multiple instances of objects in various scenarios, while maintaining specificity for structures of interest. The algorithm is based on using extremal regions as candidates for detection, followed by evaluating these regions with trained parameters. A key element of the algorithm is its built-in non-overlapping constraint, which enables effective handling of particle clustering. Experimental results on various microscopy datasets confirm the method's robustness to changes in image intensity, particle density, and size. The proposed algorithm serves as a valuable tool in the development of object detection methods for microscopy images and can be applied in both scientific and medical research.
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