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Microscopic Image Segmentation Using Hybrid Technique for Dengue Prediction

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Hybrid Soft Computing for Image Segmentation

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Abstract

An application of hybrid soft computing technique for early detection and treatment of a most common mosquito-borne viral disease Dengue, is discussed thoroughly in this chapter. The global pictures of dengue endemics are shown clearly. The structure of dengue virus and the infection procedure of the virus are also discussed. A detailed analysis of dengue illness, diagnosis methods, and treatments has been done to conclude that platelet counting is needful for early diagnosis of Dengue illness and for monitoring the health status of the patients. The main challenge in developing an automated platelet counting system for efficient, easy, and fast detection of dengue infection as well as treatment, is in the segmentation of platelets from microscopic images of a blood smear. This chapter shows how the challenges can be overcome. Color-based segmentation and k-means clustering cannot provide desired outputs in all possible situations. A hybrid soft computing technique efficiently segments platelet and overcomes the shortcomings of the other two segmentation techniques. This technique is the combination of fuzzy c-means technique and adaptive network-based fuzzy interference system (ANFIS). We have applied three different segmentation techniques namely color-based segmentation, k-means, and the hybrid soft computing technique on poor intensity images. However, only the hybrid soft computing technique detects the platelets correctly.

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References

  1. Global strategy for dengue prevention and control 2012–2020; World Health Organization (2012). ISBN 978 92 4 150403 4

    Google Scholar 

  2. Handbook for clinical management of dengue; World Health Organization (2012). ISBN 978 92 4 150471 3

    Google Scholar 

  3. Global report on dengue, WHO. www.who.int/mediacentre/factsheets/fs117/en/ (Updated February 2015). Accessed 4th Feb 2016

  4. Wang, W., Song, H., Zhao, Q.: A modified watersheds image segmentation algorithm for blood cell. In: International Conference on Communications, Circuits, and Systems Proceedings, vol. 1, Guilin (2006)

    Google Scholar 

  5. Sharif, J.M., Miswan, M.F., Ngadi, M.A., Salam, S.H., bin Abdul Jamil, M.M.: Red blood cell segmentation using masking and watershed algorithm: a preliminary study. In: International Conference on Biomedical Engineering (ICoBE), Penang, pp. 258–262, 27–28 Feb 2012

    Google Scholar 

  6. Karunakar, Y., Dr Kuwadekar, A.: An unparagoned application for red blood cell counting using marker controlled watershed algorithm for android mobile. In: Fifth International Conference on Next Generation Mobile Applications and Services, Cardiff, pp. 100–104 (2011)

    Google Scholar 

  7. Guan, P.P., Yan, H.: Blood cell image segmentation based on the hough transform and fuzzy curve tracing. In: Proceedings of the 2011 International Conference on Machine Learning and Cybernetics, Guilin, 10–13 July 2011

    Google Scholar 

  8. Venkatalakshmi, B., Thilagavathi, K.: Automatic red blood cell counting using hough transform. In: Proceedings of the 2013 IEEE Conference on Information and Communication Technologies (ICT 2013)

    Google Scholar 

  9. Kareem, S., Morling, R.C.S., Kale, I.: A novel method to count the red blood cells in thin blood films. In: IEEE International Symposium on Circuits and Systems (ISCAS) (2011)

    Google Scholar 

  10. Mohamed, M.M.A., Far, B.: A fast technique for white blood cells nuclei automatic segmentation based on Gram–Schmidt orthogonalization. In: IEEE 24th International Conference on Tools with Artificial Intelligence, Athens, pp. 947–952 (2012)

    Google Scholar 

  11. Liao, Q., Deng, Y.: An accurate segmentation method for white blood cell images. In: Proceedings of the IEEE International Symposium on Biomedical Imaging (2002)

    Google Scholar 

  12. Mohapatra, S., Patra, D., Kumar, K.: Unsupervised leukocyte image segmentation using rough fuzzy clustering. In: International Scholarly Research Network ISRN Artificial Intelligence, vol. 2012, Article ID 923946

    Google Scholar 

  13. Rovithakis, G.A., Maniadakis, M., Zervakis, M.: A hybrid neural network/genetic algorithm approach to optimizing feature extraction for signal classification. In: IEEE Transactions on Systems, Man, and Cybernetics Part B: Cybernetics, pp. 695–703, vol. 34, no. 1, Feb 2004

    Google Scholar 

  14. Laosai, J., Chamnongthai, K.: Acute leukemia classification by using SVM and K-means clustering. In: Proceedings of the International Electrical Engineering Congress (2014)

    Google Scholar 

  15. Simmons, C.P., Farrar, J.J., van VinhChau, N., Wills, B.: Dengue. N. Engl. J. Med. 366, 1423–1432 (2012)

    Article  Google Scholar 

  16. WHO.: Dengue and Severe Dengue (Fact Sheet No. 117, Revised January 2012). World Health Organization, Geneva (2012)

    Google Scholar 

  17. Shepard, D.S., et al.: Cost-effectiveness of apaediatric dengue vaccine. Vaccine 22, 1275–1280 (2004)

    Article  Google Scholar 

  18. Dengue: prevention and control, World Health Organisation, 21 Nov 2014

    Google Scholar 

  19. Update on the Dengue situation in the Western Pacific Region; WHO, 17th Nov 2015

    Google Scholar 

  20. Dengue Cases and Deaths in the Country since 2009; National Vector Borne Disease Control Programme, Directorate General of Health Services, Ministry of Health & Family Welfare, Govt. of India. http://nvbdcp.gov.in/den-cd.html. (Accessed 3rd Dec 2015)

  21. Dengue Viruses. http://www.nature.com/scitable/topicpage/dengue-viruses-22400925

  22. Dengue Transmission. http://www.nature.com/scitable/topicpage/dengue-transmission-22399758

  23. Eckert, R.L., Rorke, E.A.: Molecular biology of keratinocyte differentiation. Env. Health Perspect. 80, 109–116 (1989)

    Article  Google Scholar 

  24. Chomiczewska, D., Trznadel-Budko, E., Kaczorowska, A., Rotsztejn, H.: The role of Langerhans cells in the skin immune system. Pol Merkur Lekarski 26(153), 173–177 (2009)

    Google Scholar 

  25. Dengue Viruses. http://www.nature.com/scitable/topicpage/dengue-viruses-22400925

  26. Host Response to the dengue virus. http://www.nature.com/scitable/topicpage/host-response-to-the-dengue-virus-22402106

  27. Dengue guidelines for diagnosis, treatment, prevention and control, A joint publication of the World Health Organization (WHO) and the Special Programme for Research and Training in Tropical Diseases (TDR) (2009)

    Google Scholar 

  28. http://www.who.int/denguecontrol/faq/en/index2.html

  29. Rigau-Prez, J.G., et al.: Dengue and dengue haemorrhagic fever. Lancet 352, 971977 (1998)

    Google Scholar 

  30. Kalayanarooj, S., et al.: Early clinical and laboratory indicators of acute dengue illness. J. Infect. Dis. 176, 313–321 (1997)

    Article  Google Scholar 

  31. Balmaseda, A., et al.: Assessment of the World Health Organization scheme for classification of dengueseverity in Nicaragua. Am. J. Trop. Med. Hyg. 73, 1059–1062 (2005)

    Google Scholar 

  32. Lum, L.C.S., et al.: Quality of life of dengue patients. Am. J. Trop. Med. Hyg. 78(6), 862–867 (2008)

    Google Scholar 

  33. Cao, X.T., et al.: Evaluation of the World Health Organization standard tourniquet test in the diagnosis ofdengue infection in Vietnam. Trop. Med. Int. Health 7, 125–132 (2002)

    Article  Google Scholar 

  34. Srikiatkhachorn, A., et al.: Natural history of plasma leakage in dengue hemorrhagic fever: a serial ultrasonic study. Pediatric Infect. Dis. J. 26(4), 283–290 (2007)

    Article  Google Scholar 

  35. Nimmannitya, S., et al.: Dengue and chikungunya virus infection in man in Thailand, 196264. Observations onhospitalized patients with haemorrhagic fever. Am. J. Trop. Med. Hyg. 18(6), 954–971 (1969)

    Google Scholar 

  36. Clinical Practice Guidelines on Management of DengueInfection in Adults (Revised 2nd Edition). Ministry of Health Malaysia, Academy of Medicine Malaysia (2010)

    Google Scholar 

  37. Morens, D.M.: Dengue outbreak investigation group. Dengue in Puerto Rico: public health response to characterize and control an epidemic of multiple serotypes. Am. J. Trop. Med. Hyg. 1986(35), 197–211 (1977)

    Google Scholar 

  38. Wilder-Smith, A., Earnes, A., Paton, N.I.: Use of simple laboratory features to distinguish the early stage of severe acute respiratory syndrome from dengue fever. Clin. Infect. Dis. 39(12), 1818–1823 (2004)

    Article  Google Scholar 

  39. Kularatne, S.A., et al.: Concurrent outbreaks of Chikungunya and Dengue fever in Kandy, Sri Lanka, 2006–2007: a comparative analysis of clinical and laboratory features. Postgrad. Med. J. 2009(85), 342–346 (1005)

    Google Scholar 

  40. Yamamoto, K., et al.: Chikungunya fever from Malaysia. Intern. Med. 49(5), 501–505 (2010)

    Article  Google Scholar 

  41. Cabie, A., et al.: Dengue or acute retroviral syndrome? Presse Med. 29(21), 1173–1174 (2000)

    Google Scholar 

  42. Martinez, E.: Diagnstico diferencial. In: Dengue, pp. 189–195. Rio de Janeiro, Fiocruz (2005)

    Google Scholar 

  43. Dietz, V.J., et al.: Diagnosis of measles by clinical case definition in dengue endemic areas: implications for measles surveillance and control. Bull. World Health Organ. 70(6), 745–750 (1992)

    MathSciNet  Google Scholar 

  44. Flannery, B., et al.: Referral pattern of leptospirosis cases during a large urban epidemic of dengue. Am. J. Med. Trop. Hyg. 65(5), 657–663 (2001)

    Google Scholar 

  45. Mcfarlane, M.E.C., Plummer, J.M., Leake, P.A., Powell, L., Chand, V., Chung, S., Tulloch, K.: Dengue fever mimicking acute appendicitis: a case report. Int. J. Surg. Case Rep. 4(11), 1032–1034 (2013)

    Article  Google Scholar 

  46. Guideline for clinical management of Dengue Fever, Dengue Haemorrhagic Fever and Dengue Shock Syndrome, Directorate of National Vector Borne Diseases Control Programme, Government of India

    Google Scholar 

  47. Bain, B.J., Bates, I., Laffan, M.A., Lewis, S.M.: Dacie and Lewis Practical Haematology. Elsevier Churchill Livingstone, Edinburgh (2012)

    Google Scholar 

  48. Dung, N.M., Day, N.P., Tam, D.T.: Fluid replacement in dengue shock syndrome: a randomized, double-blind comparison of four intravenous fluid regimens. Clin. Infect. Dis. 29, 787–794 (1999)

    Article  Google Scholar 

  49. Ngo, N.T., Cao, X.T., Kneen, R.: Acute management of dengue shock syndrome: a randomized double-blind comparison of 4 intravenous fluid regimens in the first hour. Clin. Infect. Dis. 32, 204–213 (2001)

    Article  Google Scholar 

  50. Wills, B.A., et al.: Comparison of three fluid solutions for resuscitation in dengue shock syndrome. N. Engl. J. Med. 353, 877–889 (2005)

    Article  Google Scholar 

  51. Hung, N.T., et al.: Volume replacement in infants with dengue hemorrhagic fever/dengue shock syndrome. Am. J. Trop. Med. Hyg. 74, 684–691 (2006)

    Google Scholar 

  52. Wills, B.A.: Management of dengue. In: Halstead, S.B. (ed.) Dengue, pp. 193–217. Imperial College Press, London (2008)

    Chapter  Google Scholar 

  53. Dey, R., Roy, K., Bhattacharjee, D., Nasipuri, M., Ghosh, P.: An automated system for segmenting platelets from microscopic images of blood cells. In: 2015 International Symposium on Advanced Computing and Communication (ISACC), India (2015)

    Google Scholar 

  54. Zalizam, T., Mudaa, T., Salamb, R.A.: Blood cell image segmentation using hybrid K-means and median-cut algorithms. In: 2011 IEEE International Conference on Control System, Computing and Engineering, Penang, pp. 237–243

    Google Scholar 

  55. Jain, R., Kasturi, R., Schunck, B.G.: Machine Vision, Chap. 4 Image Filtering (1995)

    Google Scholar 

  56. Kaur, A., Kranthi, B.V.: Comparison between YCbCr color space and CIELab color space for skin color segmentation. Int. J. Appl. Inf. Syst. (IJAIS) 3(4) (2012). (ISSN: 2249-0868 Foundation of Computer Science FCS, New York, USA, July 2012)

    Google Scholar 

  57. Wang, W., Zhang, Y., Li, Y., Zhang, X.: The global fuzzy c-means clustering algorithm. In: Proceedings of the 6th World Congress on Intelligent Control and Automation, Dalian, China, 21–23 June, 2006

    Google Scholar 

  58. Garg, V.K., Dr. Bansal, R.K.: Soft computing technique based on ANFIS for the early detection of sleep disorders. In: International Conference on Advances in Computer Engineering and Applications (ICACEA 2015), IMS Engineering College, Ghaziabad, India

    Google Scholar 

  59. AbdulRazzaq, M., Ariffin, A.K., El-Shafie, A., Abdullah, S., Sajuri, Z.: Prediction of fatigue crack growth rate using rule-based systems. In: 4th International Conference on Modeling, Simulation and Applied Optimization (ICMSAO) (2011)

    Google Scholar 

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Acknowledgments

Authors of this chapter are paying their thanks to the Department of Bio-Technology, Govt. of India for sanctioning and funding the project (Letter No - BT/PR8456/MED/29/739/2013).

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Authors and Affiliations

  1. RCC Institute of Information Technology, Kolkata, 700015, India

    Pramit Ghosh

  2. Jadavpur University, Kolkata, 700032, India

    Ratnadeep Dey, Kaushiki Roy, Debotosh Bhattacharjee & Mita Nashipuri

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  1. Pramit Ghosh
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  2. Ratnadeep Dey
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  4. Debotosh Bhattacharjee
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  5. Mita Nashipuri
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Correspondence to Pramit Ghosh .

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Editors and Affiliations

  1. Department of Information Technology, RCC Institute of Information Technology, Kolkata, West Bengal, India

    Siddhartha Bhattacharyya

  2. Department of Computer and System Sciences, Visva-Bharati University, Santiniketan, West Bengal, India

    Paramartha Dutta

  3. Department of Computer Science and Engineering, Cooch Behar Govt. Engineering College, Cooch Behar, West Bengal, India

    Sourav De

  4. Department of Strategic Development, Raiffeisenbank Austria, Zagreb, Croatia

    Goran Klepac

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Ghosh, P., Dey, R., Roy, K., Bhattacharjee, D., Nashipuri, M. (2016). Microscopic Image Segmentation Using Hybrid Technique for Dengue Prediction. In: Bhattacharyya, S., Dutta, P., De, S., Klepac, G. (eds) Hybrid Soft Computing for Image Segmentation. Springer, Cham. https://doi.org/10.1007/978-3-319-47223-2_5

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  • DOI: https://doi.org/10.1007/978-3-319-47223-2_5

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