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

By Yongbum Lee, T. Hara, Hiroshi Fujita. IEEE Transactions on Medical Imaging, 2001

525 citations recorded by the source corpus.

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. The authors 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, the authors 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 the authors' scheme (i.e., a detection rate of about 72%), with the number of false positives at approximately 1.1/sectional image. The authors' present results show that their scheme can be regarded as a technique for CAD systems to detect nodules in helical CT pulmonary images.

Subfields: Artificial Intelligence, Pulmonary and Respiratory Medicine, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/ff898096e8ec15fbd448aa480908c7f71a3f3756

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In Artificial Intelligence

In Pulmonary and Respiratory Medicine

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

Computer analysis of computed tomography scans of the lung: a survey (2006, methodology): “a) Candidate detection schemes: For finding nodule candidates, the following techniques have been reported: multiple gray-level thresholding [111]–[113], mathematical morphology [114]–[117], genetic algorithm template matching of Gaussian spheres and discs [118], [119], clustering [120]–[123], conne”
Optimal Wavelet Transform for the Detection of Microaneurysms in Retina Photographs (2008, methodology): “A related approach has already been proposed to detect pulmonary nodules in Helical CT Images, using a Gaussian function as template [21].”
Segmentation of Pulmonary Nodules in Thoracic CT Scans: A Region Growing Approach (2008, methodology): “For this reason, several methods for developing computer aided detection (CAD) methods of nodules have been reported in the literature to assist radiologists [1]–[6].”
Comparing and combining algorithms for computer-aided detection of pulmonary nodules in computed tomography scans: The ANODE09 study (2010, background): “A large number of systems for nodule detection have been proposed in the literature (Li et al., 2008; Arimura et al., 2004; Armato et al., 2001, 2002; Bae et al., 2005; Bellotti et al., 2007; Brown et al., 2003; Dehmeshki et al., 2007; Enquobahrie et al., 2007; Farag et al., 2004; Ge et al., 2005; K”
Pulmonary nodules at chest CT: effect of computer-aided diagnosis on radiologists' detection performance. (2004, background): “In the studies of Giger et al (9), Armato et al (12), and Lee et al (15), the researchers made clear reference to true-positive rates of 94%, 72%, and 72%, respectively, and false-positive rates of 0.08, 4.60, and 1.10 nodules per section , respectively, with the CAD system.”
Current status and future potential of computer-aided diagnosis in medical imaging. (2005, background): “A number of investigators [50–71] have attempted to develop CAD schemes for computerised detection of lung nodules using various methods and techniques.”
Shape-Based Computer-Aided Detection of Lung Nodules in Thoracic CT Images (2009, methodology): “For model-based detection methods, techniques such as template-matching [7], [8], object-based deformation [9], and”
CE-Net: Context Encoder Network for 2D Medical Image Segmentation (2019, methodology): “Previous approaches to medical image segmentation are often based on edge detection and template matching [15].”