Advertisement

Firefly Algorithm-Based Kapur’s Thresholding and Hough Transform to Extract Leukocyte Section from Hematological Images

  • Venkatesan RajinikanthEmail author
  • Nilanjan Dey
  • Ergina Kavallieratou
  • Hong Lin
Chapter
Part of the Springer Tracts in Nature-Inspired Computing book series (STNIC)

Abstract

Computerized disease examination techniques are widely adopted in the literature to evaluate a considerable number of medical images ranging from the RGB scale to gray scale. This work proposes a novel image extraction method by combining Kapur’s thresholding and Hough transform (HT) to extract the leukocyte segment from the RGB-scaled blood smear image (BSI). Automated mining of the leukocyte region is always preferred in medical clinics for fast disease examination and treatment for the planning process. This study aims to implement a hybrid procedure to extort the leukocyte segment. Kapur’s is considered to enhance the RGB-scaled test image, and the HT is used to detect and extract the circle section from the image. In this work, the hematological images of leukocyte images for segmentation and classification (LISC) database are adopted for the examination. The extracted leukocyte picture is then evaluated with ground truth, and the essential image performance parameters (IPP) are then computed. This work is then validated against the semiautomated approaches, such as Shannon’s entropy-based Chan-Vese and level-set segmentation techniques existing in the literature. The outcome of the proposed techniques confirms that proposed procedure gives better IPP values compared to the existing semiautomated techniques.

Keywords

Firefly algorithm Kapur’s entropy Hough transform Hematological images Leukocyte segmentation 

References

  1. 1.
    Ashour AS et al (2015) Computed tomography image enhancement using cuckoo search: a log transform based approach. J Signal Inf Process 6(3):244–257.  https://doi.org/10.4236/jsip.2015.63023CrossRefGoogle Scholar
  2. 2.
    Dey N et al (2013) Retention of electrocardiogram features insignificantly devalorized as an effect of watermarking for a multimodal biometric authentication system. Adv Biom Secur Hum Authentication Recognit 175Google Scholar
  3. 3.
    Kar R, Saha S, Bera SK, Kavallieratou E, Bhateja V, Sarkar R (2019) Novel approaches towards slope and slant correction for tri-script handwritten word images. Imaging Sci J 67(3):159–170CrossRefGoogle Scholar
  4. 4.
    Koubarakis M et al (2018) AI in Greece: the case of research on linked geospatial data. AI Mag 39(2):91–96CrossRefGoogle Scholar
  5. 5.
    Karampidis K, Kavallieratou E, Papadourakis G (2018) A review of image steganalysis techniques for digital forensics. J Inf Secur Appl 40:217–235Google Scholar
  6. 6.
    Kavallieratou E, Likforman-Sulem L, Vasilopoulos N (2018) Slant removal technique for historical document images. J Imaging 4(6):80CrossRefGoogle Scholar
  7. 7.
    Satapathy SC, Rajinikanth V (2018) Jaya algorithm guided procedure to segment tumor from brain MRI. J Optim 2018:12.  https://doi.org/10.1155/2018/3738049CrossRefzbMATHGoogle Scholar
  8. 8.
    Raja NSM, Rajinikanth V, Fernandes SL, Satapathy SC (2017) Segmentation of breast thermal images using Kapur’s entropy and hidden Markov random field. J Med Imaging Health Inform 7(8):1825–1829CrossRefGoogle Scholar
  9. 9.
    Fernandes SL, Rajinikanth V, Kadry S (2019) A hybrid framework to evaluate breast abnormality. IEEE Consum Electron Mag.  https://doi.org/10.1109/MCE.2019.2905488CrossRefGoogle Scholar
  10. 10.
    Wang Y et al (2019) Classification of mice hepatic granuloma microscopic images based on a deep convolutional neural network. Appl Soft Comput 74:40–50.  https://doi.org/10.1016/j.asoc.2018.10.006CrossRefGoogle Scholar
  11. 11.
    Wang Y et al (2019) Morphological segmentation analysis and texture-based support vector machines classification on mice liver fibrosis microscopic images. Curr Bioinform 14(4):282–294.  https://doi.org/10.2174/1574893614666190304125221CrossRefGoogle Scholar
  12. 12.
    Rajinikanth V, Dey N, Kumar R, Panneerselvam J, Raja NSM (2019) Fetal head periphery extraction from ultrasound image using jaya algorithm and Chan-Vese segmentation. Procedia Comput Sci 152:66–73.  https://doi.org/10.1016/j.procs.2019.05.028CrossRefGoogle Scholar
  13. 13.
    Rajinikanth V, Dey N, Satapathy SC, Ashour AS (2018) An approach to examine magnetic resonance angiography based on Tsallis entropy and deformable snake model. Futur Gener Comput Syst 85:160–172CrossRefGoogle Scholar
  14. 14.
    Dey N et al (2014) Firefly algorithm for optimization of scaling factors during embedding of manifold medical information: an application in ophthalmology imaging. J Med Imaging Health Inform 4(3):384–394.  https://doi.org/10.1166/jmihi.2014.1265CrossRefGoogle Scholar
  15. 15.
    Dey N, Rajinikanth V, Ashour AS, Tavares JMRS (2018) Social group optimization supported segmentation and evaluation of skin melanoma images. Symmetry 10(2):51.  https://doi.org/10.3390/sym10020051CrossRefzbMATHGoogle Scholar
  16. 16.
    Moraru L, Obreja CD, Dey, N, Ashour AS (2018) Dempster-shafer fusion for effective retinal vessels’ diameter measurement. Soft Comput Based Med Image Anal 149–160Google Scholar
  17. 17.
    Dey N, Shi F, Rajinikanth V (2019) Leukocyte nuclei segmentation using entropy function and Chan-Vese approach. Inf Technol Intell Transp Syst 314:255–264.  https://doi.org/10.3233/978-1-61499-939-3-255CrossRefGoogle Scholar
  18. 18.
    Raja NSM, Arunmozhi S, Lin H, Dey N, Rajinikanth V (2019) A study on segmentation of leukocyte image with Shannon’s entropy. Histopathol Image Anal Med Decis Mak, 1–27.  https://doi.org/10.4018/978-1-5225-6316-7.ch001
  19. 19.
    Sghaier S, Farhat W, Souani C (2018) Novel technique for 3D face recognition using anthropometric methodology. Int J Ambient Comput Intell 9(1):60–77.  https://doi.org/10.4018/ijaci.2018010104CrossRefGoogle Scholar
  20. 20.
    Hemalatha S, Anouncia SM (2016) A computational model for texture analysis in images with fractional differential filter for texture detection. Int J Ambient Comput Intell 7(2):93–113.  https://doi.org/10.4018/IJACI.2016070105CrossRefGoogle Scholar
  21. 21.
    Hu J, Fan XP, Liu S, Huang L (2019) Robust target tracking algorithm based on superpixel visual attention mechanism: robust target tracking algorithm. Int J Ambient Comput Intell 10(2):1–17.  https://doi.org/10.4018/IJACI.2019040101CrossRefGoogle Scholar
  22. 22.
    Yang XS (2010) Engineering optimization: an Introduction with metaheuristic applications. Wiley & Sons, New JerseyCrossRefGoogle Scholar
  23. 23.
    Yang XS (2010) Firefly algorithm, Lévy flights and global optimization. In: Bramer M, Ellis R, Petridis M (eds) Research and development in intelligent systems XXVI. Springer, London.  https://doi.org/10.1007/978-1-84882-983-1_15Google Scholar
  24. 24.
    Gandomi AH, Yang X-S, Talatahari S, Alavi AH (2013) Firefly algorithm with chaos. Commun Nonlinear Sci Numer Simul 18(1):89–98MathSciNetCrossRefGoogle Scholar
  25. 25.
    Tilahun SL, Ngnotchouye JMT (2017) Firefly algorithm for discrete optimization problems: A survey. KSCE J Civ Eng 21(2):535–545CrossRefGoogle Scholar
  26. 26.
    Fister I, Yang X-S, Brest J (2013) A comprehensive review of firefly algorithms. Swarm Evol Comput 13:34–46.  https://doi.org/10.1016/j.swevo.2013.06.001CrossRefGoogle Scholar
  27. 27.
    Fister I, Yang X-S, Fister D, Fister I (2014) Firefly algorithm: a brief review of the expanding literature. In: Cuckoo search and firefly algorithm. Springer. pp 347–360.  https://doi.org/10.1007/978-3-319-02141-6-17
  28. 28.
    Yang XS, He X. Why the Firefly Algorithm Works?  https://doi.org/10.1007/978-3-319-67669-2_11arXiv:1806.01632%5bcs.NE
  29. 29.
    Dey N (ed) (2017) Advancements in applied metaheuristic computing. IGI GlobalGoogle Scholar
  30. 30.
    Raja NSM, Manic KS, Rajinikanth V (2013) Firefly algorithm with various randomization parameters: an analysis. Lect Notes Comput Sci 8297:110–121.  https://doi.org/10.1007/978-3-319-03753-0_11MathSciNetCrossRefGoogle Scholar
  31. 31.
    Raja NSM, Rajinikanth V, Latha K (2014) Otsu based optimal multilevel image thresholding using firefly algorithm. Model Simul Eng, 2014:17. Article ID 794574Google Scholar
  32. 32.
    Xu L, Oja E (1993) Randomized hough transform (RHT): basic mechanisms, algorithms, and computational complexities. CVGIP: Image Underst 57(2):131–154.  https://doi.org/10.1006/ciun.1993.1009CrossRefGoogle Scholar
  33. 33.
    Xu L, Oja E, Kultanen K (1990) A new curve detection method: randomized hough transform (RHT). Pattern Recogn Lett 11(5):331–338.  https://doi.org/10.1016/0167-8655(90)90042-ZCrossRefzbMATHGoogle Scholar
  34. 34.
    Illingworth J, Kittler J (1988) A survey of the Hough transform. Comput Vis, Graph, Image Process 44(1):87–116.  https://doi.org/10.1016/S0734-189X(88)80033-1CrossRefGoogle Scholar
  35. 35.
    Mukhopadhyay P, Chaudhuri BB (2015) A survey of Hough transform. Pattern Recogn 48(3):993–1010CrossRefGoogle Scholar
  36. 36.
    Venkatalakshmi B, Thilagavathi K (2013) Automatic red blood cell counting using Hough transform. In. IEEE conference on information and communication technologies, pp 267–271.  https://doi.org/10.1109/cict.2013.6558103
  37. 37.
    Bagui OK, Zoueu JT (2014) Red blood cells counting by circular Hough transform using multispectral images. J Appl Sci 14:3591–3594.  https://doi.org/10.3923/jas.2014.3591.3594CrossRefGoogle Scholar
  38. 38.
    Cuevas E, Díaz M, Manzanares M, Zaldivar D, Perez-Cisneros M (2013) An improved computer vision method for white blood cells detection. Comput Math Methods Med 2013:14. Article ID 137392. http://dx.doi.org/10.1155/2013/137392MathSciNetzbMATHGoogle Scholar
  39. 39.
    Prinyakupt J, Pluempitiwiriyawej C (2015) Segmentation of white blood cells and comparison of cell morphology by linear and naïve Bayes classifiers. BioMed Eng OnLine 14:63.  https://doi.org/10.1186/s12938-015-0037-1CrossRefGoogle Scholar
  40. 40.
    Rezatofighi SH, Soltanian-Zadeh H (2011) Automatic recognition of five types of white blood cells in peripheral blood. Comput Med Imaging Graph 35(4):333–343CrossRefGoogle Scholar
  41. 41.
  42. 42.
    Yang XS (2008) Nature-inspired metaheuristic algorithms, Luniver PressGoogle Scholar
  43. 43.
    Alomari YM, Abdullah SNHA, Azma RZ, Omar K (2014) Automatic detection and quantification of WBCs and RBCs using iterative structured circle detection algorithm. Comput Math Methods Med 2014:979302.  https://doi.org/10.1155/2014/979302CrossRefzbMATHGoogle Scholar
  44. 44.
    Metzler R, Klafter J (2000) The random walk’s guide to anomalous diffusion: a fractional dynamics approach. Phys Rep 339(1):1–77MathSciNetCrossRefGoogle Scholar
  45. 45.
    Nurzaman SG, Matsumoto Y, Nakamura Y, Shirai K, Koizumi S, Ishiguro H (2011) From L´evy to Brownian: a computational model based on biological fluctuation. PLoS ONE 6(2). Article ID e16168CrossRefGoogle Scholar
  46. 46.
    Raja NSM, Rajinikanth V (2014) Brownian distribution guided bacterial foraging algorithm for controller design problem. Adv Intell Syst Comput 248:141–148.  https://doi.org/10.1007/978-3-319-03107-1_17CrossRefGoogle Scholar
  47. 47.
    Rajinikanth V, Satapathy SC, Dey N, Fernandes SL, Manic KS (2019) Skin melanoma assessment using Kapur’s entropy and level set—A study with bat algorithm. Smart Innov, Syst Technol 104:193–202.  https://doi.org/10.1007/978-981-13-1921-1_19CrossRefGoogle Scholar
  48. 48.
    Shriranjani D, Tebby SG, Satapathy SC, Dey N, Rajinikanth V (2018) Kapur’s entropy and active contour-based segmentation and analysis of retinal optic disc. Lect Notes Electr Eng 490:287–295.  https://doi.org/10.1007/978-981-10-8354-9_26CrossRefGoogle Scholar
  49. 49.
    Rajinikanth V, Satapathy SC, Fernandes SL, Nachiappan S (2017) Entropy based segmentation of tumor from brain MR images–A study with teaching learning based optimization. Pattern Recogn Lett 94:87–95.  https://doi.org/10.1016/j.patrec.2017.05.028CrossRefGoogle Scholar
  50. 50.
    Rajinikanth V, Satapathy SC, Dey N, Lin H (2018) Evaluation of ischemic stroke region from CT/MR images using hybrid image processing techniques. In: Intelligent multidimensional data and image processing. pp 194–219.  https://doi.org/10.4018/978-1-5225-5246-8.ch007
  51. 51.
    Kapur JN, Sahoo PK, Wong AKC (1985) A new method for gray-level picture thresholding using the entropy of the histogram. Comput Vis Graph Image Process 29:273–285CrossRefGoogle Scholar
  52. 52.
    Manic KS, Priya RK, Rajinikanth V (2016) Image multithresholding based on Kapur/Tsallis entropy and firefly algorithm. Indian J Sci Technol 9(12):89949Google Scholar
  53. 53.
    Cherabit N, Chelali FZ, Djeradi A (2012) Circular hough transform for iris localization. Sci Technol 2(5):114–121.  https://doi.org/10.5923/j.scit.20120205.02CrossRefGoogle Scholar
  54. 54.
    Likforman-Sulem L, Kavallieratou E (2018) Document image processing. J Imaging 4(7):84CrossRefGoogle Scholar
  55. 55.
    Duda RO, Hart PE (1972) Use of the Hough transformation to detect lines and curves in pictures. Commun ACM 15:11–15CrossRefGoogle Scholar
  56. 56.
    Manic KS, Rajinikanth V, Ananthasivam S, Suresh U (2015) Design of controller in double feedback control loop–an analysis with heuristic algorithms. Chem Prod Process Model 10(4):253–262.  https://doi.org/10.1515/cppm-2015-0005CrossRefGoogle Scholar
  57. 57.
    Jagatheesan K, Anand B, Samanta S, Dey N, Ashour AS, Balas VE (2019) Design of a proportional-integral-derivative controller for an automatic generation control of multi-area power thermal systems using firefly algorithm. IEEE/CAA J Autom Sin 6(2):503–515.  https://doi.org/10.1109/jas.2017.7510436CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Venkatesan Rajinikanth
    • 1
    Email author
  • Nilanjan Dey
    • 2
  • Ergina Kavallieratou
    • 3
  • Hong Lin
    • 4
  1. 1.St. Joseph’s AI GroupSt. Joseph’s College of EngineeringChennaiIndia
  2. 2.Department of Information TechnologyTechno India College of TechnologyKolkataIndia
  3. 3.Department of Information and Communication Systems EngineeringUniversity of the AegeanSamosGreece
  4. 4.Department of Computer Science & Engineering TechnologyUniversity of Houston-DowntownHoustonUSA

Personalised recommendations