  Image segmentation, the process of partitioning an image into meaningful
regions, plays a pivotal role in computer vision and medical imaging
applications. Unsupervised segmentation, particularly in the absence of labeled
data, remains a challenging task due to the inter-class similarity and
variations in intensity and resolution. In this study, we extract high-level
features of the input image using pretrained vision transformer. Subsequently,
the proposed method leverages the underlying graph structures of the images,
seeking to discover and delineate meaningful boundaries using graph neural
networks and modularity based optimization criteria without relying on
pre-labeled training data. Experimental results on benchmark datasets
demonstrate the effectiveness and versatility of the proposed approach,
showcasing competitive performance compared to the state-of-the-art
unsupervised segmentation methods. This research contributes to the broader
field of unsupervised medical imaging and computer vision by presenting an
innovative methodology for image segmentation that aligns with real-world
challenges. The proposed method holds promise for diverse applications,
including medical imaging, remote sensing, and object recognition, where
labeled data may be scarce or unavailable. The github repository of the code is
available on [https://github.com/ksgr5566/unseggnet]
