Lung Nodule Detection using Soft-Computing based Imaging Practice

Lung is the one of the vital internal organs responsible to supply the oxygen to other body parts throughout the life span. In humans, the abnormalities in lung arise due to various reasons and the timely screening of the lung abnormality will help to regulate/cure the disease. The common infections in lung are due to tuberculosis, pneumonia and cancer. In which, the Lung-Cancer (LC) is very cruel and untreated LC will lead to death. The LC is normally seen as a large Lung-Nodule (LN) and in clinical diagnosis, the detection and categorization of the LN plays a major role in disease detection and treatment planning process. The proposed work aims to implement a methodology to extract the LN from the Computed-Tomography (CT) image with a considerable accuracy. This work implements Social-Group-Optimization (SGO) and Kapur's threshold (SGO+KE) to enhance the CT image. Later the Active-Contour (AC) and Watershed-Segmentation (WS) is executed to extract the LN. The merit of the proposed work is confirmed based on the performance measures attained with the proposed tool.