Medical image segmentation with fuzzy C-Means and kernelized fuzzy C-Means hybridized on PSO and QPSO

Medical image segmentation is a key step towards medical image analysis. The objective of medical image segmentation is to delineate Region Of Interests (ROI) from the images. Hybridization of nature inspired algorithms with soft computing provides accurate image segmentation results in less computation time. In this work, various algorithms for medical image segmentation which help medical practitioners for better diagnosis and treatment are discussed and the following global optimized clustering techniques are proposed; Fuzzy C-Means (FCM) optimized with Particle Swarm Optimization (PSO), Kernelized FCMPSO (KFCMPSO), FCM optimized with Quantum PSO (FCMQPSO) and KFCMQPSO to extract ROI from the medical images. The experiments were conducted on Magnetic Resonance Imaging (MRI) images and analysis were carried out with respect to average intra cluster distance, elapsed time/computation time and Davies Bouldin Index (DBI). The conventional FCM is noted to be more sensitive to noise and shows poor segmentation performance on the images corrupted by noise. The experimental results showed that the proposed hybridized FCM and KFCM with PSO and QPSO performs well with good convergence speed. The convergence speed is found to be approximately three units lesser than other algorithms.