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An Effective Segmentation Approach for Lung CT Images Using Histogram Thresholding with EMD Refinement

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 216)

Abstract

Image segmentation is an important step in extracting information from medical images. Segmentation of pulmonary chest computed tomography (CT) images is a precursor to most pulmonary image analysis. The purpose of lung segmentation is to separate the voxels corresponding to lung tissue from the surrounding anatomy. This paper presents an automated CT lung image segmentation. The approach utilizes histogram-based thresholding with Earth Mover’s Distance (HTEMD)-based refinement methods. The final segmented output is further refined by morphological operators. The performance of HTEMD is compared with Otsu’s, K-Means, and histogram thresholding using fuzzy measures.

Keywords

Computed tomography Lung nodule Earth mover’s distance Histogram thresholding 

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Copyright information

© Springer India 2014

Authors and Affiliations

  1. 1.Research Scholar, Anna University of TechnologyTirunelveliIndia
  2. 2.Department of Computer Science and EngineeringAnna University of TechnologyTirunelveliIndia

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