LUNG NODULE DETECTION IN CT SCAN IMAGES

Abstract

Timely detection of pulmonary nodule plays significant role in the diagnosis of lung cancer at early stage and thus improves the chances of survival of a patient. In this paper a computer aided nodule detection method is proposed for the segmentation and detection of challenging nodules like juxtavascular and juxtapleural. Lungs are segmented from computed tomography (CT) images using intensity thresholding, initial analysis of CT image histogram is done to select a suitable threshold value for better segmentation results. Simple morphological closing is used to include juxtapleural nodules in segmented lung regions. K-means clustering is applied for the initial detection and segmentation of potential nodules. Shape specific morphological opening is implemented to refine segmentation outcomes. These segmented potential nodules are then divided into six groups on the basis of their thickness and percentage connectivity with lung walls. Grouping not only helped in improving system’s efficiency but also reduced computational time, otherwise consumed in calculating and analyzing unnecessary features for all nodules. Different sets of 2D and 3D features are extracted from nodules in each group to eliminate false positives. Small size nodules are differentiated from false positives (FPs) on the basis of their salient features, sensitivity of the system for small nodules is 81.81%. SVM classifier is used for the classification of large nodules, achieved sensitivity for the proposed system is 91.4% applying 10-fold cross-validation. Receiver Operating Characteristic (ROC) curve is used for the analysis of CAD system. Overall sensitivity achieved by the system is 88.2% with 2.49 FPs per case, accuracy is 95.73%.