114 Copyright © 2017, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited. Chapter 6 DOI: 10.4018/978-1-5225-2229-4.ch006 ABSTRACT The popularity of clustering in segmentation encouraged us to develop a new medical image segmenta- tion system based on two-hybrid clustering techniques. Our medical system provides an accurate detec- tion of brain tumor with minimal time. The hybrid techniques make full use of merits of these clustering techniques and overcome the shortcomings of them. The first is based on K-means and fuzzy C-means (KIFCM). The second is based on K-means and particle swarm optimization (KIPSO). KIFCM helps Fuzzy C-means to overcome the slow convergence speed. KIPSO provides global optimization with less time. It helps K-means to escape from local optima by using particle swarm optimization (PSO). In addition, it helps PSO to reduce the computation time by using K-means. Comparisons were made between the proposed techniques and K-means, Fuzzy C-means, expectation maximization, mean shift, and PSO using three benchmark brain datasets. The results clarify the effectiveness of our second pro- posed technique (KIPSO). INTRODUCTION Image segmentation is a fundamental and critical task in image processing. In most cases, segmentation is a pre-step for many image processing applications. Therefore, if the segmentation is accurate, then also other tasks that depend on it will be accurate. It refers to the process of partitioning a digital image into multiple non-overlapping regions to be more understandable and meaningful (Bai & Wang, 2014). There Segmentation of Brain Tumor from MRI Images Based on Hybrid Clustering Techniques Eman A. Abdel Maksoud Mansoura University, Egypt Mohammed Elmogy Mansoura University, Egypt Rashid Mokhtar Al-Awadi Mansoura University, Egypt