Performance Evaluation of MRI Tumor Segmentation Using Clustering Algorithms

Background: Magnetic resonance imaging (MRI) segmentation assumes great importance in research and clinical applications. The brain segmentation using MRI is challenging due to a significant amount of noise caused by operator performance, scanner, and the environment, which can lead to serious inaccuracies with segmentation. Evaluations of segmentation results in medical imaging are caused by the absence of a gold standard. So, the performance evaluation of these methods would be necessary. Methods: In this paper, the performance of clustering algorithms such as Fuzzy C-Means (FCM), Hard C-Means (HCM), and Neural Gas (NG) for tumor detection is evaluated on 100 downloaded images. For this purpose, we evaluated these 3 algorithms under noise condition, convergence speed. Compared with manual segmentation by an expert radiologist, sensitivity, specificity, and accuracy are calculated for each segmentation methods. Results: It can be stated, based on the results, that among the HCM and NG algorithms, the highest degree of accuracy and robustness to noise belongs to FCM. Moreover, optimum convergence rate and iteration need to gain final result using FCM algorithm. Conclusion: All the quantitative performance analysis and visual comparisons clearly demonstrated the superiority of FCM algorithm for MRI-based tumor detection.