Analysis of Lung Tumour Detection and Segmentation Using Level Set Method of Active Contour Model

Abstract

Lung cancer is the most common leading cancer in both men and women all over the world. Accurate image segmentation is an essential image analysis tool that is responsible for partitioning an image into several sub-regions. Active contour model have been widely used for effective image segmentation methods as this model produce sub-regions with continuous boundaries. It is used in the applications such as image analysis, deep learning, computer vision and machine learning. Advanced level set method helps to implement active contours for image segmentation with good boundary detection accuracy. This paper proposes a model based on active contour using level set methods for segmentation of such lung CT images and focusing 3D lesion refinement. The features were determined by applying a multi-scale Gaussian filter. This proposed method is able to detect lung tumors in CT images with intensity, homogeneity and noise. The proposed method uses LIDC-IDRI dataset images to segment accurate 3D lesion of lung tumor CT images.