Tytuł artykułu
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Warianty tytułu
Języki publikacji
Abstrakty
Effective segmentation of thermal images reflecting the inflamed region in human body to assist medical diagnosis is a challenging task. In this paper we propose a method for thermal image segmentation, named as ‘‘Region shrinking based Accurate Segmentation of Inflammatory areas from Thermograms’’, in short RASIT. The method comprising of four steps encompassing thermal image contextual electrostatic force extraction, intensity adjustment as applicable, automated generation of the weighted threshold, and segmentation of thermograms based on the computed threshold. The proposed method is operative devoid of the subjective and possibly questionable task of parameter selection clearly offering an edge over the state-of-the-art methods in terms of usage. The efficacy of our proposed technique is shown by experimenting on abnormal thermograms taken from two datasets: one is newly created knee arthritis thermogram dataset and another is online available Database of Mastology Research (DMR) of breast thermograms. The averages on correct detection rates obtained by the proposed method for both the knee and breast thermograms are 98.2% and 96.98% respectively with favorable inference on basis of Wilcoxon's test. Application of the proposed method minimizes the complexity of parameter selection, time complexity of execution and amount of under segmentation compared to existing state-of-the-art methods of thermogram segmentation.
Wydawca
Czasopismo
Rocznik
Tom
Strony
903--917
Opis fizyczny
Bibliogr. 38 poz., rys., tab., wykr.
Twórcy
autor
- Department of Computer Science and Engineering, Tripura University, Tripura, India
autor
- Department of Computer Science and Engineering, Tripura University, Suryamaninagar 799022, Tripura, India
autor
- Department of Computer Science and Engineering, Tripura University, Tripura, India
autor
- Department of Computer Science and Engineering, Jadavpur University, West Bengal, India
Bibliografia
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- [8] Dieppe P. Epidemiology of the rheumatic diseases Second Edition. AJ Silman, MC Hochberg (eds). Oxford: Oxford University Press, 2001, pp. 377, £95.00. ISBN: 0192631497. IntJ Epidemiol 2002;(31):1079–80. http://dx.doi.org/10.1093/ije/31.5.1079-a.
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- [10] Bhowmik MK, Bardhan S, Das K, Bhattacharjee D, Nath S. Pain related inflammation analysis using infrared images. Thermosense: thermal infrared applications XXXVIII 2016 2016. http://dx.doi.org/10.1117/12.2223425.
- [11] Mital M, Scott EP. Thermal detection of embedded tumors using infrared imaging. J Biomech Eng 2007;129:33. http://dx.doi.org/10.1115/1.2401181.
- [12] Jadin MS, Taib S. Infrared image enhancement and segmentation for extracting the thermal anomalies in electrical equipment. Electron Electr Eng 2012;120. http://dx.doi.org/10.5755/j01.eee.120.4.1465.
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- [14] Fan S, Yang S. Infrared electric image segmentation using fuzzy Renyi entropy and chaos differential evolution algorithm. International Conference on Future Computer Sciences and Application 2011. http://dx.doi.org/10.1109/icfcsa.2011.57.
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- [17] Snekhalatha U, Anburajan M, Sowmiya V, Venkatraman B, Menaka M. Automated hand thermal image segmentation and feature extraction in the evaluation of rheumatoid arthritis. Proc Inst Mech Eng H: J Eng Med 2015;229:319–31. http://dx.doi.org/10.1177/0954411915580809.
- [18] Shahari S, Wakankar A. Color analysis of thermograms for breast cancer detection. International Conference on Industrial Instrumentation and Control (ICIC) 2015. http://dx.doi.org/10.1109/iic.2015.7151001.
- [19] Ramirez-Rozo TJ, Garcia-Alvarez JC, Castellanos-Dominguez CG. Infrared thermal image segmentation using expectation-maximization-based clustering.. XVII Symposium of Image Signal Processing and Artificial Vision (STSIVA) 2012. http://dx.doi.org/10.1109/stsiva.2012.6340586.
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- [21] Sayed GI, Soliman M, Hassanien AE. Bio-inspired swarm techniques for thermogram breast cancer detection. Med Imaging Clin Appl Stud Comput Intell 2016;487–506. http://dx.doi.org/10.1007/978-3-319-33793-7_21.
- [22] Font-Aragones X, Faundez-Zanuy M, Mekyska J. Thermal hand image segmentation for biometric recognition. IEEE Aerospace Electron Syst Mag 2013;28:4–14. http://dx.doi.org/10.1109/maes.2013.6533739.
- [23] Dutta T, Sil J, Chottopadhyay P. Condition monitoring of electrical equipment using thermal image processing. IEEE First International Conference on Control Measurement and Instrumentation (CMI) 2016. http://dx.doi.org/10.1109/cmi.2016.7413761.
- [24] Selvarasu N, Vivek S, Nandhitha N. Performance evaluation of image processing algorithms for automatic detection and quantification of abnormality in medical thermograms. International Conference on Computational Intelligence and Multimedia Applications (ICCIMA) 2007. http://dx.doi.org/10.1109/iccima.2007.216.
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- [32] Belgiu M, Draguţ L. Comparing supervised and unsupervised multiresolution segmentation approaches for extracting buildings from very high resolution imagery. ISPRS J Photogram Remote Sens 2014;96:67–75. http://dx.doi.org/10.1016/j.isprsjprs.2014.07.002.
- [33] Otsu N. A threshold selection method from gray-level histograms. IEEE Trans Syst Man Cybern 1979;9:62–6. http://dx.doi.org/10.1109/tsmc.1979.4310076.
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- [36] Oliva D, Cuevas E, Pajares G, Zaldivar D, Osuna V. A multilevel thresholding algorithm using electromagnetism optimization. Neurocomputing 2014;139:357–81. http://dx.doi.org/10.1016/j.neucom.2014.02.020.
- [37] Maulik U, Bandyopadhyay S. Performance evaluation of some clustering algorithms and validity indices. IEEE Trans Pattern Anal Mach Intell 2002;24:1650–4. http://dx.doi.org/10.1109/tpami.2002.1114856.
- [38] Wilcoxon F. Individual comparisons by ranking methods. Biometrics Bull 1945;1:80. http://dx.doi.org/10.2307/3001968.
Typ dokumentu
Bibliografia
Identyfikator YADDA
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