Image Segmentation is a fundamental task in the context of image processing and computer vision that consists of partitioning an image into subsets of pixels that share similar properties so as to facilitate the analysis and interpretation of the visual data. There are a wide range of applications for this technique, particularly for the analysis of biomedical images. In this talk I will present a new approach based on Consensus-Based Models for the Image Segmentation task [1, 3]. By considering each pixel as a particle characterize by a 2D vector position and a static feature we propose a virtual interaction scheme based on the Hegselman- Krause Model that will determine the asymptotic formation of a finite number of clusters [2]. I will discuss the application of this method to a variety of biomedical images. This work has been done with the collaboration of Fondazione Mondino.