Segmentation and classification of lung tumor from 3D CT image using K-means clustering algorithm

Abstract

Lung cancer is the abnormal growth of cells in the lung causing severe risk to human health because lung has a connected network of blood vessels and lymphatic channels vulnerable to metastasis. The survival rate of lung cancer greatly depends on the early detection and staging of the lung tumor. Computed Tomography (CT) images are widely used for lung tumor detection as they provide information about different parts of the lung. The accuracy of detecting tumor shape, location and volume plays a vital role in successful diagnosis and treatment of tumors. So, tumor detection and representation from three dimensional (3D) CT images are necessary to measure the tumor size properly and to detect the exact location of the tumor. Researchers introduced different methods for tumor nodule detection from two dimensional (2D) CT images, hence, the problems associated with such methods is the separation of lung from background and the differentiation of tumor from airways. This paper presents the use of neighborhood and connectivity properties of the CT image pixels to overcome these problems. In this work, several morphological processing used to remove background, noises and airways from the lung CT image and then the k-means clustering algorithms based segmentation used for lung tumor detection. The volumetric analysis of the lung nodules for the prediction of tumor stages performed according to the Tumor Nodule Metastasis (TNM) classification proposed by the World Health Organization (WHO). TNM for lung cancer uses the dimension, spreading and metastasis properties of the tumor. The lung tumor segmentation performed on a dataset named SPIE-AAPM Lung CT Challenge dataset collected from Washington University in St. Louis, which includes 22,489 CT images of 70 patients. The proposed scheme for 3D CT image segmentation and classification provides better performance in terms of accurate tumor detection and visualization of variant size, shape and location, less computation time and proper classification of tumor stages with an accuracy of 95.68%.