Abstract
Tuberculosis (TB) is one of the disease causing microorganisms which is infectious with a high morbidity and mortality around the world. According to the latest World Health Organization (WHO) report, there were an estimated 10.4 million new TB cases in 2015 and more than 1.8 million deaths were attributed to the disease the same year. A person with TB causing bacteria need to know the presence of the bacteria at its latent (passive) stage so that she/he can take medication only for a couple of weeks to a couple of months (daily, bi-weekly or weekly depending on the treatment regimen of choice) to be free of TB. A person who is diagnosed with an active form of TB has to take medication for a minimum of 6 to 9 months. Negligence to take this medication properly will make the TB causing bacteria drug resistant. Diagnosing TB causing bacteria in its different stages is an effective mechanism which can enhance the performance of present detection schemes available in different clinics. In that regard, this paper presents a methodology for use in enhancing TB diagnosis specificity based on image processing of lung images acquired using normal x-ray as well as those from a sputum smear microscopy. A general framework is designed preceded by a step to extract useful imaging features for use in robust characterization of latent, active, drug resistant and TB free samples.
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
References
Rasha, B., Mona, S.: Molecular characterization of clinical isolates with elevated resistance to carbapenems. Open Microbiol. 11(1), 152–159 (2017)
Clinical Disease Control: Tubeculosis Elimination Division of Tuberculosis, pp. 3–4, July 2017
Alimudin, Z., Mark, R., Richard, H., Fordhan, C.: Current concepts: tuberculosis. New Engl. J. Med. 3(10), 2–3 (2013)
Kamble, P.A., Anagire, V.V., Chamataguodar, S.N.: CXR tuberculosis detection using Matlab image proccesing. IRJET 3(06), 2342–2344 (2016)
Kandaswamy, A., Kumar, C.S., Ramanathan, R.P., Malmurugana, N.: Neural classification of lung sounds using wavelet cofficent. Comput. Biol. Med. 34(6), 523–537 (2004)
Saed, S., Aouni, L., Mac, T.: Gear fault diagnosis using time frequency methods, pp. 7–27. National Research Councile, December 2015
Costa Filho, C.F.F., Levy, P.C., Xavier, C. de M., Fujimoto, L.B.M., Costa, M.G.F.: Automatic identification of tuberculosis mycobacterium. Res. Biomed. Eng. 31(1), 33–43 (2015)
Rani, O., Biju, S., Gagan, S., Jeny, R.: A review of automatic methods based on image processing techniques for tuberculosis detection from microscopic sputum smear images. J. Med. Syst. 40, 1–13 (2015)
Manuel, G.F., Fillip, S., Gabriel, C.: Identification of TB bacteria based on shape and color. Real Time Imaging 10(4), 251–262 (2004)
Product Development Guidelines New York: WHO Tuberculosis Diagnostics Workshop, Ohio, Cleveland, 27 July 1997
Ayas, S., Ekinci, M.: Random Forest-based Tuberculosis Bacteria Classification in Images of ZN-stained Sputum Smear Samples. Sig. Image Video Process. 8(Suppl. 1), 49–61 (2014)
Chang, J., Arbeláez, P., Switz, N., Reber, C.: Automated tuberculosis diagnosis using flourescence image from a mobile microscope. Med. Image Comput. Assist. Interv. 15(3), 345–352 (2012)
Acknowledgement
This work was supported by internal student’s project at FIM, University of Hradec Kralove, Czech Republic (under ID: UHK-FIM-SP-2018).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
Samuel, T., Assefa, D., Krejcar, O. (2018). Framework for Effective Image Processing to Enhance Tuberculosis Diagnosis. In: Nguyen, N., Hoang, D., Hong, TP., Pham, H., Trawiński, B. (eds) Intelligent Information and Database Systems. ACIIDS 2018. Lecture Notes in Computer Science(), vol 10752. Springer, Cham. https://doi.org/10.1007/978-3-319-75420-8_36
Download citation
DOI: https://doi.org/10.1007/978-3-319-75420-8_36
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-75419-2
Online ISBN: 978-3-319-75420-8
eBook Packages: Computer ScienceComputer Science (R0)