Abstract Image segmentation refers to the process of partitioning an image into mutually exclusive regions. It can be considered as the most essential and crucial process for facilitating the delineation, characterization, and visualization of regions of interest in any medical image. Despite intensive research, segmentation remains a challenging problem due to the diverse image content, cluttered objects, occlusion, image noise, non-uniform object texture, and other factors. There are many algorithms and techniques available for image segmentation but still there needs to develop an efficient, fast technique of medical image segmentation. This paper presents an efficient image segmentation approach using K-means clustering technique integrated with Fuzzy C-means algorithm. It is followed by thresholding and level set segmentation stages to provide an accurate brain tumor detection. The proposed technique can get benefits of the K-means clustering for image segmentation in the aspects of minimal computation time. In addition, it can get advantages of the Fuzzy C-means in the aspects of accuracy. The performance * Corresponding author at: 44th Hamza Ibn Abdulmotaleb ST., Ahmed Maher ST., Mansoura City, Dakahlia Gov., A.R.E., Egypt. Tel.: +20 10 281 42 420. E-mail addresses: eng.eaaar@yahoo.com, eng.eman.te@gmail.com (E. Abdel-Maksoud), melmogy@mans.edu.eg (M. Elmogy), actt_egypt@ yahoo.com (R. Al-Awadi). Peer review under responsibility of Faculty of Computers and Information, Cairo University. Egyptian Informatics Journal (2015) of the proposed image segmentation approach was evaluated by comparing it with some state of the art segmentation algorithms in case of accuracy, processing time, and performance. The accuracy was evaluated by comparing the results with the ground truth of each processed image. The experimental results clarify the effectiveness of our proposed approach to deal with a higher number of segmentation problems via improving the segmentation quality and accuracy in minimal execution time. Ó 2015 Production and hosting by Elsevier B.V. on behalf
Abdel-Maksoud et al., introduced an image segmentation technique that used the integration of K-means clustering and fuzzy C-means algorithm REF .
Brain tumor segmentation based on a hybrid clustering technique
Egyptian Informatics Journal
Computer Science