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.
