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Abstrakty
The sputum smear microscopy based tuberculosis (TB) screening method is a conventional method employed for disease identification. It provides significant benefit to TB burdened communities across the globe; however, there are many challenges faced in processing the sputum smear images. When the smear is thick or uneven the number of overlapping bacilli is more which impedes the diagnosis. The separation of overlapping bacilli is significant without which the results lead to gross errors in identification of the disease causing agent. In this work, separation of overlapping bacilli is carried out by method of concavity (MOC) and is compared with the conventional methods such as multi-phase active contour (MAC) and marker-controlled watershed (MCW). Performance of the methods is evaluated based on the statistical mean quality score of shape descriptors extracted from the separated and existing true bacilli. The shape descriptors employed in this work include geometric features, Hu's, Zernike moments and Fourier descriptors. Results of separated overlapping bacilli demonstrate that MOC performs better than MAC and MCW. It is observed that the statistical mean quality score of the separated bacilli using the proposed MOC shows nearest match with true bacilli. The validation performed with experimental results to that of human annotations highlights the performance of MOC in separating the overlapping bacilli in the sputum smear images.
Wydawca
Czasopismo
Rocznik
Tom
Strony
87--99
Opis fizyczny
Bibliogr. 37 poz., rys., tab., wykr.
Twórcy
autor
- Department of Electronics and Communication Engineering, Sri Sairam Engineering College, West Tambaram, Chennai 600044, India
autor
- Department of Instrumentation Engineering, Madras Institute of Technology, Anna University, Chrompet, Chennai 600044, India
Bibliografia
- [1] Tuberculosis. WHO global tuberculosis report; 2013.
- [2] Tuberculosis fact sheet; 2013.
- [3] Veropoulos K. Machine learning approaches to medical decision making.(Ph.D. dissertation) Department of Computer Science, University of Bristol; 2001.
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- [6] Tuberculosis facts. World Health Organization; 2007.
- [7] Tadrous PJ. Computer-assisted screening of Ziehl–Neelsen- stained tissue for mycobacteria. Algorithm design and preliminary studies on 2,000 images. Am J Clin Pathol 2010;133(6):849–58.
- [8] Sotaquira M, Rueda L, Narvaez R. Detection and quantification of bacilli and clusters present in sputum smear samples: a novel algorithm for pulmonary tuberculosis diagnosis. Proc. Int. Conf. Digital Image Processing; 2009. pp. 117–21.
- [9] Costa MG, Costa-Filho CF, Sena JF, Salem J, de-Lima MO. Automatic identification of Mycobacterium tuberculosis with conventional light microscopy. Proc. IEEE Conf. Engineering in Medicine and Biology Society; 2008. pp. 382–5.
- [10] Ramana KV, Basha KS. Neural image recognition system with application to tuberculosis detection. Proc. Int. Conf. Information Technology: Coding and Computing, vol. 2; 2004. pp. 694–8.
- [11] Steingart KR, Henry M, Ng V, Hopewell PC, Ramsay A, Cunningham J, et al. Fluorescence versus conventional sputum smear microscopy for tuberculosis: a systematic review. Lancet Infect Dis 2006;6(9):570–81.
- [12] Tuberculosis diagnostics Xpert MTB/RIF test. World Health Organization; 2013.
- [13] 90-Minute TB test not a game changer for India. Indo Asian News Service; 29 August 2011.
- [14] Khutlang R, Krishnan S, Whitelaw A, Douglas TS. Automated detection of tuberculosis in Ziehl–Neelsen-stained sputum smears using two one-class classifiers. J Microsc 2010;237(1):96–102.
- [15] Forero MG, Cristobal G, Desco M. Automatic identification of Mycobacterium tuberculosis by Gaussian mixture models. J Microsc 2006;223(2):120–32.
- [16] Wu Q, Merchant FA, Castleman KR. Microscope image processing. London, UK: Elsevier; 2008.
- [17] Zhai Y, Liu Y, Zhou D, Liu S. Automatic identification of Mycobacterium tuberculosis from ZN-stained sputum smear: algorithm and system design. Proc. IEEE Int. Conf. Robotics and Biomimetrics; 2010. pp. 41–6.
- [18] Korath JM, Abbas A, Romagnoli JA. Separating touching and overlapping objects in particle images – a combined approach. Chem Eng Trans 2007;11:167–72.
- [19] Xue Q, Jones NS, Leake MC. A general approach for segmenting elongated and stubby biological objects: extending a chord length transform with the radon transform. Proc. IEEE Int. Symposium Biomedical Imaging: From Nano to Macro; 2010. pp. 161–4.
- [20] Wienert S, Heim D, Saeger K, Stenzinger A, Beil M, Hufnagl P, et al. Detection and segmentation of cell nuclei in virtual microscopy images: a minimum-model approach. Sci Rep 2012;503.
- [21] Wittenberg T, Grobe M, Munzenmayer C, Kuziela H, Spinnler K. A semantic approach to segmentation of overlapping objects. Methods Inf Med 2004;43:343–53.
- [22] Qi X, Xing F, Foran DJ, Yang L. Robust segmentation of overlapping cells in histopathology specimens using parallel seed detection and repulsive level set. IEEE Trans Biomed Eng 2012;59(3):754–65.
- [23] Park C, Huang JZ, Ji JX, Ding Y. Segmentation, inference and classification of partially overlapping nanoparticles. IEEE Trans Pattern Anal Mach Intell 2013;35(3):1–33.
- [24] Nee LH, Mashor MY, Hassan R. White blood cell segmentation for acute leukemia bone marrow images. J Med Imaging Health Inf 2012;2(3):278–84.
- [25] Zhang Y, Zhou X, Lu J, Lichtman J, Adjeroh D, Wong ST. 3D axon structure extraction and analysis in confocal fluorescence microscopy images. Neural Comput 2008;20(8):1899–927.
- [26] Sharif JM, Miswan MF, Ngadi MA, Salam MSH, Mahadi bin Abdul Jamil M. Red blood cell segmentation using masking and watershed algorithm: a preliminary study. Int. Conf. Biomedical Engineering; 2012. pp. 258–62.
- [27] Wahlby C, Lindblad J, Vondrus M, Bengtsson E, Bjorkesten L. Algorithms for cytoplasm segmentation of fluorescence labelled cells. Anal Cell Pathol 2002;24(2/3):101–11.
- [28] Farhan M, Yli-Harja O, Niemisto A. A novel method for splitting clumps of convex objects incorporating image intensity and using rectangular window-based concavity point-pair search. Pattern Recognit 2013;46(3):741–51.
- [29] Gu G, Cui D, Li X. Segmentation of overlapping leucocyte images with phase detection and spiral interpolation. Comput Methods Biomech Biomed Eng 2012;15(4):425–33.
- [30] Chan TF, Vese LA. Active contour and segmentation models using geometric PDE's for medical imaging. In: Malladi R, editor. Geometric methods in bio-medical image processing. Berlin/Heidelberg: Springer-Verlag; 2002. pp. 63–75.
- [31] Mendhurwar KA, Kakumani R, Devabhaktuni V. Microarray image segmentation using Chan–Vese active contour model and level set method. Proc. 31st Annual Int. Conf. IEEE Engineering in Medicine and Biology Society; 2009. pp. 3629–32.
- [32] Chen Q, Petriu E, Yang X. A comparative study of Fourier descriptors and Hu's seven moment invariants for image recognition. Proc. Canadian Conf. Electrical and Computer Engineering; 2004. pp. 103–6.
- [33] Huang Z, Leng J. Analysis of Hu's moment invariants on image scaling and rotation. Proc. 2nd Int. Conf. Computer Engineering and Technology; 2010. pp. 476–80.
- [34] Arvacheh EM, Tizhoosh HR. Pattern analysis using Zernike moments. Proc. IEEE Instrumentation and Measurement Technology Conf.; 2005. pp. 1574–8.
- [35] Tahmasbi A, Saki F, Shokouhi SB. Classification of benign and malignant masses based on Zernike moments. Comput Biol Med 2011;41(8):726–35.
- [36] Chang J, Arbelaez P, Switz N, Reber C, Tapley A, Davis JL, et al. Automated tuberculosis diagnosis using fluorescence images from a mobile microscope. Med Image Comput Comput Assist Interv 2012;15(3):345–52.
- [37] Wu X, Shah SK. Level set with embedded conditional random fields and shape priors for segmentation of overlapping objects. Proc. 10th Asian Conf. Computer Vision. Berlin/Heidelberg: Springer-Verlag; 2011. pp. 230–41.
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Bibliografia
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