The Medical Images Segmentation Utility (MIS-U)

Medical image segmentation plays a crucial role in diagnosing and modeling anatomical and functional structures of organs. Region-based segmentation methods, especially clustering techniques like K-Means, Fuzzy C-Means, Expectation Maximization, and Histogram Quantization, are widely used due to their adaptability across various imaging modalities. However, segmentation outcomes vary depending on the clustering method and parameters used, making reproducibility a challenge. To address this, the MIS-U Imaging and Clustering Suite is introduced—a versatile software tool for visualizing, clustering, segmenting, and exporting medical imaging data, with a particular emphasis on diffusion tensor imaging (DTI). The suite includes dedicated utilities for image clustering, anatomical segmentation using binary layers, and exporting data for simulation and analysis. By standardizing the preprocessing and clustering workflow and incorporating advanced concepts like Unistable and Unistable 3D representations, MIS-U provides a consistent, flexible environment for medical image segmentation and modeling tasks.