Adaptive Thresholding Technique for Segmentation and Juxtapleural Nodules Inclusion in Lung Segments

Abstract

Early diagnosis of lung cancer plays crucial role in the improvement of patients' chances of survival. Computer aided detection (CAD) system has been a groundbreaking step in the timely diagnosis and identification of potential nodules (lesions). CAD system starts detection process by extracting lung regions from CT scan images, this step narrows down the region for detection. Hence saving the time consumption and reducing false positives outside the lung regions that results in the improvement of specificity of system. Proper lung segmentation significantly increases the performance of CAD systems. Different algorithms are presented by various researchers to improve segmentation results. An intensity based approach is presented in this paper for the segmentation of parenchyma and the goal is to achieve reasonable segmentation results in less time. Algorithm used in this paper is based on the Intensity based thresholding which is the fastest method for image segmentation. Images used in this research to analyze algorithm's result are taken from Lung Image Database Consortium (LIDC). Twenty random cases were picked, each having different number of slices (128 to 300). Algorithm is implemented using MatlabR2014 and a system with processor of 2.6 GHz and RAM of 4 GB. Total time taken for a single case of 128 images was 6.3 seconds and hence with an average of 49 milli sec/slice.