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

Abstract EN

MedLCai segmentation is a key step towards medical image analysis.

The object^^ve of medical image segmentation ^l^o delJZ/ateRegion 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 V/ich help medical practitioners for better diagnosis and treatment are discussed and the following global optimized cjustering techniques are proposed; Fuzzy C-Means optimized with Particle Swarm Optimization (FCMPSO), Kernelized Fuzzy^-Meahs optimized with PSO (KFCMPSO), Fuzzy C-Means optimized with Quantum PSO (FCMQPSO) and KFCMQPSO jy extract ROI from the medical images.

The experiments were conducted on Magnetic Resonance Imaging (MRI) imOges analysis were carried out with respect to average intra cluster distance, elapsed time/computation time and DavieswouldinIndex (DBI).

The conventional FCM is noted to be more sensitive to noise and shows poor segmentation performance/q\ the/images corrupted by noise.

The experimental results showed that the proposed hybridized FCM and KFCM with PSOq/d QPSOjBerforms well with good convergence speed.

The convergence speed is found to be approximately three units lesser than/^iheralgorithms.