Elastic Model Fuzzy Control Based Lung Lobe Segmentation from MDCT Images

Abstract

Human lung is divided into five distinct anatomic compartments called lobes. Segmenting lung lobes from Multidetector-row computed tomography (MDCT) images can provide useful information for surgical operation and diagnosis of pathology. We have proposed a method for segmenting lungs lobes from MDCT images. The method estimates boundary surface between the lung lobes based on lobar fissure and tubular tissues (blood vessels and bronchi). The method can find the boundary surfaces within the error of 1.5 mm. Almost of errors are caused by fixing nodes outside the lung. This paper proposes a new moving method of nodes in order to improve the lung lobe segmentation algorithm.