Development of Segmentation and Classification Algorithm for Lung Cancer Tumor Detection Using CT scan Images and Performance Analysis

Abstract

Lung cancer is one of the main causes of death in the world. The identification of lung cancer tumor at initial stage is of extreme importance, if it is intended to high mortality rate. Early detection and diagnosis of human beings. To identify lung cancer tumor, Computed Tomography (CT) scan images are broadly used to distinguish the disease, with the help of expert physicians. Time factor plays very important role in the diagnosis of the abnormal cells growth, as it is directly related to survival rate. In this paper, presented a fully automatic framework for lung cancer nodule detection from CT scan images.  A Threshold based technique is proposed to identify and separate the candidate nodule from other structures. The main objective is noise removal operation, thresholding, gray scale imaging, histogram equalization, texture segmentation and morphological operation. The image processing techniques are mainly used for detection of lung cancer. Medical imaging is developing quickly due to evolutions in image processing techniques, as well as image analysis, recognition and image enhancement. The proposed method is evaluated using large lung database which is collected from lung Image Database Consortium (LIDC) using image processing toolbox in MATLAB. The Proposed method achieved excellent results compared with other existing segmentation methods .