Abstract
An ancient disease, tuberculosis (TB) remains one of the major causes of disability and death worldwide. In 2006, 9.2 million new cases of TB emerged and killed 1.7 million people. We report on the development of tools to help in the detection of lesions and nodules from High Resolution Computed Tomography (HRCT) scans and changes in total lesion volumes across a study. These automated tools are designed to assist radiologists, clinicians and scientists assess patients’ responses to therapies during clinical studies. The tools are centered upon a rule-based system that initially segments the lung from HRCT scans and then categorizes the different components of the lung as normal or abnormal. A layered segmentation process, utilizing a combination of adaptive thresholding, three-dimensional region growing and component labeling is used to successively peel off outside entities, isolating lung and trachea voxels. Locating the Carina allows logical labeling of the trachea and left/right lungs. Shape and texture analysis are used to validate and label normal vascular tree voxels. Remaining abnormal voxels are clustered on density, gradient and texture-based criteria. Several practical problems that arise due to large changes in lung morphology due to TB and patients’ inability to hold their breath during scan operations need to be addressed to provide a viable computational solution. Comparisons of total common volumes of lesions by size for a given patient across multiple visits are in concordance with expert radiologist’s manual measurements.
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
Ceresa, M., Artaechevarria, X., Munoz-Barrutia, A., Ortiz-de-Solorzano, C.: Automatic leakage detection and recovery for airway tree extraction in chest CT images. In: 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, April 14-17, pp. 568–571 (2010)
Gao, Q., Wang, S., Zhao, D., Liu, J.: Accurate Lung Segmentation For X-ray CT Images. In: Third International Conference on Natural Computation, ICNC 2007, August 24-27, vol. 2, pp. 275–279 (2007)
Guo, S., Wu, X.: Automatic Segmentation and Quantitative Diagnosis of Pulmonary Parenchyma in Thoracic CT. In: The 1st International Conference on Bioinformatics and Biomedical Engineering, ICBBE 2007, July 6-8, pp. 668–670 (2007)
Hu, S., Hoffman, E.A., Reinhardt, J.M.: Automatic lung segmentation for accurate quantitation of volumetric X-ray CT images. IEEE Transactions Medical Imaging 20(6), 490–498 (2001)
Kim, H.S., Yoon, H.-S., Trung, K.N., Lee, G.S.: Automatic Lung Segmentation in CT Images Using Anisotropic Diffusion and Morphology Operation. In: 7th IEEE International Conference on Computer and Information Technology, CIT 2007, October 16-19, pp. 557–561 (2007)
Kim, H., Maekado, M., Tan, J.K., Ishikawa, S., Tsukuda, M.: Automatic extraction of ground-glass opacity shadows on CT images of the thorax by correlation between successive slices. In: 17th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2005, November 16, p. 607– 612 (2005)
Kim, H., Nakashima, T., Itai, Y., Maeda, S., Tan, J.K., Ishikawa, S.: Automatic detection of ground glass opacity from the thoracic MDCT images by using density features. In: International Conference on Control, Automation and Systems, ICCAS 2007, October 17-20, pp. 1274–1277 (2007)
Kim, R., Dasovich, G., Bhaumik, R., Brock, R., Furst, J.D., Raicu, D.S.: An investigation into the relationship between semantic and content based similarity using LIDC. In: Proceedings of the International Conference on Multimedia Information Retrieval (MIR 2010), pp. 185–192. ACM, New York (2010)
Li, Q., Li, F., Suzuki, K., Shiraishi, J., Abe, H., Engelmann, R., Nie, Y., MacMahon, H., Doi, K.: Computer-Aided Diagnosis in Thoracic CT. Seminars in Ultrasound, CT, and MRI 26(5), 357–363 (2005); Update of Chest Imaging-Part I
NIAID, (2010) weblink, http://www.niaid.nih.gov/topics/tuberculosis/Pages/Default.aspx (accessed August 31, 2010)
Rubin, G.D.: Data explosion: the challenge of multidetector-row CT. European Journal of Radiology 36(2), 74–80 (2000)
Sluimer, I., Schilham, A., Prokop, M., van Ginneken, B.: Computer analysis of computed tomography scans of the lung: a survey. IEEE Transactions on Medical Imaging 25(4), 385–405 (2006)
Tong, J., Zhao, D.-Z., Wei, Y., Zhu, X.-H., Wang, X.: Computer-Aided Lung Nodule Detection Based on CT Images. In: IEEE/ICME International Conference on Complex Medical Engineering, CME 2007, May 23-27, pp. 816–819 (2007)
Tong, J., Zhao, D.-Z., Yang, J.-Z., Wang, X.: Automated Detection of Pulmonary Nodules in HRCT Images. In: The 1st International Conference on Bioinformatics and Biomedical Engineering, ICBBE 2007, July 6-8, pp. 833–836 (2007)
van Rikxoort, E.M., de Hoop, B., van de Vorst, S., Prokop, M., van Ginneken, B.: Automatic Segmentation of Pulmonary Segments From Volumetric Chest CT Scans. IEEE Transactions on Medical Imaging 28(4), 621–630 (2009)
van Rikxoort, E.M., Prokop, M., de Hoop, B., Viergever, M.A., Pluim, J., van Ginneken, B.: Automatic Segmentation of Pulmonary Lobes Robust Against Incomplete Fissures. IEEE Transactions on Medical Imaging 29(6), 1286–1296 (2010)
van Rikxoort, E.M., Goldin, J.G., van Ginneken, B., Galperin-Aizenberg, M., Ni, C., Brown, M.S.: Interactively learning a patient specific k-nearest neighbor classifier based on confidence weighted samples. In: 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, April 14-17, pp. 556–559 (2010)
Zhou, J., Chang, S., Metaxas, D.N., Zhao, B., Ginsberg, M.S., Schwartz, L.H.: An Automatic Method for Ground Glass Opacity Nodule Detection and Segmentation from CT Studies. In: 28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2006, August 30-September 3, pp. 3062–3065 (2006)
This research was partially supported by the Intramural Research Program of the National Institutes of Health, National Institute of Allergy and Infectious Diseases, and the Bill & Melinda Gates Foundation and Wellcome Trust through the Grand Challenges in Global Health Initiative (PI, Douglas Young, Imperial College, London)
This research was primarily supported by the National Institutes of Health, National Institute of Allergy and Infectious Diseases, Office of Cyber Informatics and Computational Biology funding to NET ESOLUTIONS CORPORATION (NETE)
This research utilizes proprietary computational algorithms and software tools developed by the NET ESOLUTIONS CORPORATION (NETE) team
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag GmbH Berlin Heidelberg
About this paper
Cite this paper
Lahouar, S. et al. (2012). Automatic Extraction and Categorization of Lung Abnormalities from HRCT Data in MDR/XDR TB Patients. In: Gaol, F., Nguyen, Q. (eds) Proceedings of the 2011 2nd International Congress on Computer Applications and Computational Science. Advances in Intelligent and Soft Computing, vol 145. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28308-6_48
Download citation
DOI: https://doi.org/10.1007/978-3-642-28308-6_48
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-28307-9
Online ISBN: 978-3-642-28308-6
eBook Packages: EngineeringEngineering (R0)