Automated segmentation of lung field in HRCT images using active shape model

Abstract

Lung field segmentation is a prerequisite for the development of automated computer aided diagnosis of interstitial lung diseases from chest high resolution computed tomography (HRCT) images. The traditional threshold-based or region growing methods tends to fail when dense pathology is present in the lung field. In such cases, it is found that prior shape knowledge increases the efficiency and accuracy of segmentation. The main contribution of this paper is the automated segmentation of lung field from HRCT images. The method is based on active shape model and it is able to segment the lung fields from HRCT images with various pathological regions like honeycomb, cavity, ground glass opacity (GGO), consolidation etc. Multiple atlases of the the lung fields are created using training data of size 100 for both left and right lungs. These atlases are used for the training of active shape model for estimation of lung shape fields. In this paper, after training, the seed selection part is automated in the matching step to ensure minimal human intervention. The result of segmentation is evaluated in terms of the Jaccard index, Dice Similarity Coefficient (DSC) and Modified Hausdorff Distance (MHD) for 80 HRCT slices from a publicly available database. The experimental results proves that the proposed method is reliable for segmentation of pathological lung fields in HRCT images with improved accuracy.