GUBS: Graph-Based Unsupervised Brain Segmentation in MRI Images.

Brain segmentation in magnetic resonance imaging (MRI) images is the process of isolating the brain from non-brain tissues to simplify the further analysis, such as detecting pathology or calculating volumes. This paper proposes a Graph-based Unsupervised Brain Segmentation (GUBS) that processes 3D MRI images and segments them into brain, non-brain tissues, and backgrounds. GUBS first constructs an adjacency graph from a preprocessed MRI image, weights it by the difference between voxel intensities, and computes its minimum spanning tree (MST).

Threshold estimation based on local minima for nucleus and cytoplasm segmentation.

Background: Image segmentation is the process of partitioning an image into separate objects or regions. It is an essential step in image processing to segment the regions of interest for further processing. We propose a method for segmenting the nuclei and cytoplasms from white blood cells (WBCs).