This paper offers an in-depth comparative evaluation of the K-means (K-MEANS) and Expectation-Maximization (EM) algorithms for spine image segmentation. The research aims to assess the algorithms' performance regarding accuracy, robustness, and computational efficiency. Utilizing a dataset of spine images, the study compares segmentation outcomes through various quantitative metrics. The analysis reveals the relative strengths and limitations of K-MEANS and EM, providing valuable insights into their effectiveness in clinical applications. These findings support the enhancement of spine image segmentation techniques, contributing to progress in medical imaging and healthcare

K-Means Algorithm, Expectation-maximization Algorithm, Segmentation accuracy, Computational Efficiency, Comparative study