Automatic Segmentation of Lung Nodule From CT Images Using Fuzzy C-Means Clustering Algorithm and Active Contour Model

Abstract

Lung nodule segmentation is a major part in computer-aided diagnosis (CAD) system for lung cancer detection and diagnosis. The key issue in CAD of lung nodule is to correct and accelerate rapid segmentation of diseased tissue. This paper provides a novel approach method to segment the lung nodules using region based active contour model and Fuzzy C-Means clustering technique. Computed Tomography (CT) imaging is much efficient for lung cancer diagnosis and detection. Fuzzy c-means clustering algorithm (FCM) is sensitive to noise, local spatial information is often introduced to improve the robustness of the FCM algorithm for image segmentation. The methodology involves image acquisition, seeks the contour of the object using active contour model and segmentation of lung nodule is performing by using fuzzy cmeans clustering algorithm. The experimental results of this method show that it is an effective algorithm and produces highest accuracy in the segmentation of lung nodules.