Abstract
This paper describes a novel discrimination method of lung cancers based on statistical analysis of thoracic computed tomography (CT) scans. Our previous Computer-Aided Diagnosis (CAD) system can detect lung cancers from CT scans, but, at the same time, yields many false positives. In order to reduce the false positives, the method proposed in the present paper uses a relationship between lung cancers, false positives and image information on CT scans. The trend of variation of the relationships is acquired through statistical analysis of a set of CT scans prepared for training. In testing, by use of the trend, the method predicts the appearance of lung cancers and false positives in a CT scan, and improves the accuracy of the previous CAD system by modifying the system’s output based on the prediction. The method is applied to 218 actual thoracic CT scans with 386 actual lung cancers. Receiver operating characteristic (ROC) analysis is used to evaluate the results. The area under the ROC curve (Az) is statistically significantly improved from 0.918 to 0.931.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Weir, H.K.: Annual report to the nation on the status of cancer, 1975–2000. Journal National Cancer Institute 95(17), 1276–1299 (2003)
Tanaka, T., Yuta, K., Kobayashi, Y.: A study of false-negative case in mass-screening of lung cancer. Jay. J. Thor. Med. 43, 832–838 (1984)
Yamamoto, S., et al.: Image Processing for Computer-Aided Diagnosis of Lung Cancer by CT(LSCT). Systems and Computers in Japan 25(2), 67–80 (1994)
Henschke, C.I., et al.: Early Lung Cancer Action Project: overall design and findings from baseline screening. Lancet 354(9173), 99–105 (1999)
Yamamoto, S., et al.: Quoit Filter: A New Filter Based on Mathematical Morphology to Extract the Isolated Shadow, and Its Application to Automatic Detection of Lung Cancer in X-Ray CT. In: Proc. 13th Int. Conf. Pattern Recognition II, pp. 3–7 (1996)
Okumura, T., et al.: Variable NQuoit filter applied for automatic detection of lung cancer by X-ray CT. In: Computer Assisted Radiology and Surgery (CAR’98), pp. 242–247 (1998)
Sato, Y., et al.: Three-dimensional multiscale line filter for segmentation and visualization of curvilinear structures in medical images. Medical Image Analysis 2(2), 143–168 (1998)
Giger, M.L., Bae, K.T., MacMahon, H.: Computerized detection of pulmonary nodules in CT images. Investigative Radiology 29(4), 459–465 (1994)
Lee, Y., et al.: Automated Detection of Pulmonary Nodules in Helical CT Images Based on an Improved Template-Matching Technique. IEEE Transactions on Medical Imaging 20(7), 595–604 (2001)
McNitt-Gray, M.F., et al.: Computer-Aided Techniques to Characterize Solitary Pulmonary Nodules Imaged on CT. In: Computer-Aided Diagnosis in Medical Imaging, pp. 101–106. Elsevier, Amsterdam (1999)
Armato III, S.G., et al.: Computerized detection of lung nodules on CT scans. Radio Graphics 19(5), 1303–1311 (1999)
Suzuki, K., et al.: Massive training artificial neural network (MTANN) for reduction of false positives in computerized detection of lung nodules in low-dose computed tomography. Medical Physics 30(7), 1602–1617 (2003)
Suzuki, K., Horiba, I., Sugie, N.: Neural Edge Enhancer for Supervised Edge Enhancement from Noisy Images. IEEE Transaction on Pattern Analysis and Machine Intelligence 25(12), 1582–1596 (2003)
Kawata, Y., et al.: Quantitative surface characterization of pulmonary nodules based on thin-section CT images. IEEE Transaction Nuclear Science 45, 2132–2138 (1998)
McCulloch, C.C., et al.: Model-Based Detection of Lung Nodules in Computed Tomography Exams. Academic Radiology 11, 258–266 (2004)
Takizawa, H., et al.: Recognition of Lung Nodule Shadows from Chest X-ray CT Images Using 3D Markov Random Field Models. Systems and Computers in Japan 35(8), 1401–1412 (2004)
Takizawa, H., Yamamoto, S.: Construction Method of Threedimensional Deformable Template Models for Tree-shaped Organs. IEICE Transactions on Information and Systems E89-D-II(1), 326–331 (2006)
Fukano, G., et al.: Eigen Image Recognition of Pulmonary Nodules from Thoracic CT Images by Use of Subspace Method. IEICE Transactions on Information and Systems E88-D-II(6), 1273–1283 (2005)
Fukano, G., et al.: Recognition method of lung nodules using blood vessel extraction techniques and 3D object models. In: Proc. of Society of Photo-Optical Instrumentation Engineers, Medical Imaging 2003, pp. 190–198 (2003)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer Berlin Heidelberg
About this paper
Cite this paper
Takizawa, H., Yamamoto, S., Shiina, T. (2007). Accuracy Improvement of Lung Cancer Detection Based on Spatial Statistical Analysis of Thoracic CT Scans. In: Gagalowicz, A., Philips, W. (eds) Computer Vision/Computer Graphics Collaboration Techniques. MIRAGE 2007. Lecture Notes in Computer Science, vol 4418. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71457-6_56
Download citation
DOI: https://doi.org/10.1007/978-3-540-71457-6_56
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-71456-9
Online ISBN: 978-3-540-71457-6
eBook Packages: Computer ScienceComputer Science (R0)