LUNG FIELD SEGMENTATION ON COMPUTED TOMOGRAPHY IMAGE USING REGISTRATION

Abstract

During recent years, research on lung nodules detection is still much discussed by the researchers. Automatic lung nodule detection generally consists of two steps, namely lung nodule candidate detection and classification to determine nodule. Detection of nodule candidates begins with segmenting lung field. Current method is widely used for lung field segmentation of CT scan image is Thresholding and Active Contour, which relies on contrast of gray values between lung parenchyma and surrounding tissues. Drawback of both methods is that if nodule is large and located on borders of lung, then nodules will not be included in the image of lung (nodules contained will be lost). This means that segmentation of lung field was considered a failure, because image of nodule is object of attention will be lost. Purpose of this research is lung field segmentation that contain abnormalities in CT scan image using registration methods. Registration is used namely elastic registration (non-rigid registration). We also perform lung fields segmentation using thresholding and Active Contour, as a comparison with method we proposed. Results of our study show that segmentation with registration approach has accuracy 95.5%, sensitivity 91.5%, and specificity of 96.6%. Segmentation with threshold has accuracy 92.2%, sensitivity 91.3%, and specificity 92.4%. While segmentation with Active Contour has accuracy 92.4%, sensitivity 87.5%, and specificity of 93.7%.
Keywords: active contour, non-rigid registration, lung field, thresholding.