EMD Based Curve Evolution Method for Segmentation of Lungs on CT Scan Images

Abstract

Medical imaging is the technique that is used to produce images of the human body or parts for clinical purpose. The CT image provides thorough information of structure of lungs, which could be used for better surgical preparation for treating Lung Cancer features. This work proposes a method for segmentation of lungs from the given CT images. Here a curve evolutional framework for segmentation is used. The flow fields driving the curves are based on the distributions of features in the inner and outer regions bounded by the curves. The segmentation method is automatic and shows good result. Computer Tomography (CT) is one of the most efficient medical diagnostic methods and has currently a widespread usage. Multi-slice CT scanning technology has revolutionized screening of the lungs and inspires necessitate for pulmonary image analysis. Segmentation of the lungs and lobes is a prerequisite for such image analysis in chest CT scans. CT scan is more appropriate for showing the detailed information of the parts of human body and it is used for various applications such as detection, classification etc. The analysis of lungs in CT image is used to detect the airway and the vessel present in the lungs [6]. The human lungs are subdivided into distinct pulmonary lobes separated by thin barriers, called fissures. Each lobe contains separate supply branches for both vessels and airways. The segmentation of anatomical parts is important in the detection of pathological abnormalities present in the lungs. Segmentation of the pulmonary lobes is relevant in many clinical applications. Accurate lung segmentation allows for the detection and quantification of abnormalities within the lungs. The lobes are separately supplied by the first subdivisions of the bronchial tree after the main bronchi. The lobes function independently within the lungs. Segmentation of the pulmonary lobes is relevant in clinical practice and particularly challenging for cases with severe diseases or incomplete fissures. Pulmonary fissure is a boundary between the lobes in the lungs. Its segmentation is of clinical interest as it facilitates the assessment of lung disease on a lobar level. A new approach has been made for segmenting the major fissures in both lungs on thin-section computed tomography (CT) [5]. An image transformation called “ridge map” is proposed for enhancing the appearance of fissures on CT. A curve-growing process, modeled by a Bayesian network, is described that is influenced by both the features of the ridge map and prior knowledge of the shape of the fissure. The process is implemented in an adaptive regularization framework that balances these influences and reflects the causal dependencies in the Bayesian network using an entropy measure. This method effectively alleviates the problem of inappropriate weights of