Segmentation of CT lung nodules using FCM with firefly search algorithm

Computed Tomography (CT) lung nodules segmentation is a challenging task for detecting the nodules from images. Medical experts segment the medical images manually, that is almost not clear which leads to false detection. Conventional Fuzzy c-means is unsupervised method that has been implemented for clustering of the CT images still suffers with as low convergence rate, getting stuck in the local minima and vulnerable to initialization sensitivity. Firefly algorithm is a new population-based optimization method is used for solving many complex problems. The proposed system presents a dynamic and intelligent clustering approach called Firefly Search with Fuzzy C-Means (FSFCM) to segment the lung nodules automatically. This approach uses the capability of firefly search to find optimal initial cluster centers for the FCM and thus improve the segmentation results. The performance of proposed system is compared with other state-of-art algorithms. Experiment is carried out using real time images to investigate the proposed method.