Automated detection of pulmonary nodules in helical CT images based on an improved template-matching technique

Abstract

The purpose of this study is to develop a technique for computer-aided diagnosis (CAD) systems to detect lung nodules in helical X-ray pulmonary computed tomography CT) images. We propose a novel template-matching technique based on a genetic algorithm (GA) template matching (GATM) for detecting nodules existing within the lung area; the GA was used to determine the target position in the observed image efficiently and to select an adequate template image from several reference patterns for quick template matching. In addition, a conventional template matching was employed to detect nodules existing on the lung wall area, lung wall template matching (LWTM), where semicircular models were used as reference patterns; the semicircular models were rotated according to the angle of the target point on the contour of the lung wall. After initial detecting candidates using the two template-matching methods, we extracted a total of 13 feature values and used them to eliminate false-positive findings. Twenty clinical cases involving a total of 557 sectional images were used in this study. 71 nodules out of 98 were correctly detected by our scheme (i.e., a detection rate of about 72%), with the number of false positives at approximately 1.1/sectional image. Our present results show that our scheme can be regarded as a technique for CAD systems to detect nodules in helical CT pulmonary images.

A Comprehensive Framework for Automatic Detection of Pulmonary Nodules in Lung Ct Images

“…A template matching technique which is based on genetic algorithm was proposed in Lee et al (2001) for detecting lung nodules in chest CT scans. This method was validated on 20 clinical cases of a private dataset and a rule-based classifier was used to reduce the number of false positives (Fps).…”

Reduction of false positives at vessel bifurcations in computerized detection of lung nodules

“…A number of research groups have reported a variety of CAD systems for detecting lung nodules in chest CT images, including multiple grayscale thresholding [5,6] , local density maximum algorithm [7] , fuzzy clustering [8] , genetic algorithm template matching of Gaussian spheres and discs [9] , filters enhancing spherical structures [10][11][12][13] , curved surface morphology analysis [14] , and volumetric curvature-based thresholding and region growing [15] . Commercial CAD systems for detecting lung nodules in chest CT images have also been developed, including the ImageChecker CT Lung system (R2 Technology Inc., Sunnyvale, CA, USA), Lung VCAR (GE Healthcare Technologies, Waukesha, WI, USA), and Syngo Lung CAD (Siemens Medical Solutions, Erlangen, Germany).…”

Three-Dimensional Detection of Pulmonary Nodules in Chest CT Images

“…Kim, et al, 2003) (Brown MS, et al, 2003) (Lee Y, et al, 2001), however, is more difficult than that of isolated or juxtapleural nodules because vascular nodules are attached to the pulmonary vessels. Vascular nodules are distinguished morphologically from the vessels because they are typically spherical in shape, while the vessels are elongated.…”

“…A number of methods and systems for automated nodule detection are developed over the years (D.-Y. Kim, et al, 2003) (Brown MS, et al, 2003) (Lee Y, et al, 2001), some are focused on the "density" (considering the fact that lung nodules have relatively higher densities than those of lung parenchyma) while others are focused on "model" (considering some features like compactness, elongation and size).…”