Automated tuberculosis diagnosis using fluorescence images from a mobile microscope

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 7512 LNCS, Issue , 2012, Pages 345-352

  Chang, Jeannette ^a   Arbeláez, Pablo ^a   Switz, Neil ^b   Reber, Clay ^b   Tapley, Asa ^b,c   Davis, J Lucian ^c   Cattamanchi, Adithya ^c   Fletcher, Daniel ^b   Malik, Jitendra ^a  

^a UNIVERSITY OF CALIFORNIA   (United States)

^b UNIVERSITY OF CALIFORNIA   (United States)

^c SAN FRANCISCO GENERAL HOSPITAL   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

AUTOMATION; DIAGNOSIS; DIGITAL DEVICES; FLUORESCENCE; FLUORESCENCE MICROSCOPY; HEALTH CARE; MATHEMATICAL MORPHOLOGY; MEDICAL COMPUTING; SUPPORT VECTOR MACHINES; TEMPLATE MATCHING;

AUTOMATED PROCESSING; CLASSIFICATION PERFORMANCE; HISTOGRAMS OF ORIENTED GRADIENTS; MORPHOLOGICAL OPERATIONS; PHOTOMETRIC FEATURES; SUPPORT VECTOR MACHINE CLASSIFICATION; TUBERCULOSIS DIAGNOSIS; VISUAL IDENTIFICATION;

MEDICAL IMAGING;

ARTICLE; AUTOMATED PATTERN RECOGNITION; CYTOLOGY; EQUIPMENT DESIGN; EQUIPMENT FAILURE ANALYSIS; FLUORESCENCE MICROSCOPY; HOSPITAL INFORMATION SYSTEM; HUMAN; INSTRUMENTATION; METHODOLOGY; MICROBIOLOGY; MYCOBACTERIUM TUBERCULOSIS; PATHOLOGY; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY; SPUTUM; TUBERCULOSIS;

EQUIPMENT DESIGN; EQUIPMENT FAILURE ANALYSIS; HUMANS; MICROSCOPY, FLUORESCENCE; MYCOBACTERIUM TUBERCULOSIS; PATTERN RECOGNITION, AUTOMATED; POINT-OF-CARE SYSTEMS; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY; SPUTUM; TUBERCULOSIS;

MLCS; MLOWN;

EID: 84872973769     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-33454-2_43     Document Type: Conference Paper

References (18)

1. Boehme, C.C., Nabeta, P., Hillemann, D., Nicol, M.P., Shenai, S., Krapp, F., Allen, J., Tahirli, R., Blakemore, R., Rustomjee, R., Milovic, A., Jones, M., O’Brien, S.M., Persing, D.H., Ruesch-Gerdes, S., Gotuzzo, E., Rodrigues, C., Al-land, D., Perkins, M.D.: Rapid Molecular Detection of Tuberculosis and Rifampin Resistance. New England J. of Med. 363(11), 1005–1015 (2010)
2. Breslauer, D.N., Maamari, R.N., Switz, N.A., Lam, W.A., Fletcher, D.A.: Mobile Phone Based Clinical Microscopy for Global Health Applications. PLoS One 4(7), e6320 (2009)
3. Cattamanchi, A., Davis, J.L., Worodria, W., den Boon, S., Yoo, S., Matovu, J., Kiidha, J., Nankya, F., Kyeyune, R., Byanyima, P., Andama, A., Joloba, M., Osmond, D.H., Hopewell, P.C., Huang, L.: Sensitivity and Specificity of Fluorescence Microscopy for Diagnosing Pulmonary Tuberculosis in a High HIV Prevalence Setting. Int. J. Tuberc. Lung Dis. 13(9), 1130–1136 (2010)
4. Dalal, N., Triggs, B.: Histograms of Oriented Gradients for Human Detection. In: CVPR, pp. 886–893 (2005)
5. Chang, C.C., Lin, C.J.: LIBSVM: A Library for Support Vector Machines. ACM Trans. on Intell. Sys. and Tech. 2(3), 27:1–27:27 (2011)
6. Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20(3), 273–297 (1995)
7. Costa, M.G., Costa Filho, C.F., Sena, J.F., Salem, J., de Lima, M.O.: Automatic Identification of Mycobacterium Tuberculosis with Conventional Light Microscopy. In: 30th Ann. Int. IEEE EMBS Conf., pp. 382–385 (2008)
8. Evans, C.A.: GeneXpert-A Game-Changer for Tuberculosis Control? PLoS Med. 8, e1001064 (2011)
9. Forero, M.G., Cristóbal, G., Desco, M.: Automatic Identification of Mycobacterium Tuberculosis by Gaussian Mixture Models. J. of Microscopy 223(2), 120–132 (2006)
10. Global Tuberculosis Control: WHO Report 2011 (2011), http://www.who.int/tb/publications/global_report/
11. Hu, M.K.: Visual Pattern Recognition by Moment Invariants. IRE Trans. on Info. Theory 8(2), 179–187 (1962)
12. Khutlang, R., Krishnan, S., Dendere, R., Whitelaw, A., Veropoulos, K., Learmonth, G., Douglas, T.S.: Classification of Mycobacterium Tuberculosis in Images of ZN-Stained Sputum Smears. IEEE Trans. on Info. Tech. in Biomed. 14(4), 949–957 (2010)
13. Kivihya-Ndugga, L.E.A., van Cleeff, M.R.A., Githui, W.A., Nganga, L.W., Kibuga, D.K., Odhiambo, J.A., Klatser, P.R.: A Comprehensive Comparison of Ziehl-Neelsen and Fluorescence Microscopy for the Diagnosis of Tuberculosis in a Resource-Poor Urban Setting. Int. J. Tuberc. Lung Dis. 7(12), 1163–1171 (2003)
14. Kubica, G.P.: Mycobacterium Tuberculosis Bacteria Using Acid-Fast Ziehl-Neelsen Stain; magnified 1000x. Public Health Image Library, Centers for Disease Control and Prevention, Atlanta (1979)
15. Otsu, N.: A Threshold Selection Method from Gray-Level Histograms. IEEE Trans. on Systems, Man and Cybernetics 9(1), 62–66 (1979)
16. Platt, J.C.: Probabilistic Outputs for Support Vector Machines and Comparisons to Regularized Likelihood Methods. In: Advances in Large Margin Classifiers, pp. 61–74 (1999)
17. Fan, R.E., Chang, K.W., Hsieh, C.J., Wang, X.R., Lin, C.J.: LIBLINEAR: A Library for Large Linear Classification. JMLR 9, 1871–1874 (2008)
18. Veropoulos, K.: Machine learning approaches to medical decision making. U. of Bristol (2001)