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A new image segmentation technique using bi-entropy function minimization

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Abstract

Image segmentation, the splitting of a multispectral and panchromatic image into groups of homogeneous pixels based on the region of interest(ROI), is a universal step for many advanced image processing and object recognition. Image segmentation essentially affects the overall performance of any automated image analysis system due to utmost importance of its quality. Image segmentation can be performed by recursively splitting the whole image or by merging together a large number of minute regions until a specified condition is satisfied. Thresholding is an old, simple and important method in gray scale image segmentation. In this paper, we have used Shannon’s entropy and proposed a new multilevel thresholding image segmentation method based on minimization of bi-entropy function. A smoothing technique based on weight value of the pixel within a w × w moving window is introduced to make the splitting result continuous and qualitative. The proposed algorithm takes full account of the spatial information and the gray information to decrease the computing quantity. Standard medical images, texture images, and remote sensing images are segmented in the experiment and compared with other related segmentation methods with different measures. Experimental results show that the proposed method can quickly converge with high computational efficiency.

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References

  1. Albuquerque M, Esquef IA, Mello G, Albuquerque MP (2004) Image thresholding using Tsallisentropy. Pattern Recogn Lett 25:1059–1065

    Article  Google Scholar 

  2. Apro M et al (2013) Colour space selection for entropy-based image segmentation of folded substrate images. Acta Polytechnica Hungarica 10(1):43–62

    Google Scholar 

  3. Arifin AZ, Asano A (2006) Image segmentation by histogram thresholding using hierarchical cluster analysis. Pattern Recogn Lett 27(13):1515–521

    Article  Google Scholar 

  4. Badera A et al (2009) Image segmentation using excess entropy. J Signal Process Syst 205–214

  5. Bandyopadhyay O, Chanda B, Bhattacharya BB (2016) Automated segmentation of bones in x-ray images based on entropy measures. Int J Image Graph 16:12016:165001-1-165001-32

    Article  Google Scholar 

  6. Barbier AL et al (2010) An entropy-based approach to automatic image segmentation of satellite images. Elsevier 512–518

  7. Bongiovanni G, Cinque L, Levialdi S, Rosenfeld A (1988) Bimodality analysis using pyramids. In: Levialdi S (ed) Multi-computer vision. Academic Press, New York, pp 1–7

  8. Bongiovanni G, Cinque L, Levialdi S, Rosenfeld A (1993) Image segmentation by a multi-resolution approach. Pattern Recogn 26(12):1845–854

    Article  Google Scholar 

  9. Brink A (1995) Minimum spatial entropy threshold selection. IEEE Proc-Vis Image Signal Process 142(3):128–132

    Article  Google Scholar 

  10. Brink AD (1996) Using spatial information as an aid to maximum entropy image threshold selection. Pattern Recogn Lett 17(1):29–36 (8)

    Article  Google Scholar 

  11. Celeux G., Peyrard N, Forbes F (2003) EM procedures using mean field-like approximations for Markov model-based image segmentation. Pattern Recogn 36:131–144

    Article  MATH  Google Scholar 

  12. Chang I, Du Y, Wang J, Guo SM, Thouin PD (2006) Survey and comparative analysis of entropy and relative entropy thresholding techniques. Vis Image Signal Process IEEE Proc 153(6):837–850

    Article  Google Scholar 

  13. Chaudhuri D, Agarwal A (2010) Split-and-merge procedure for image segmentation using bimodality detection approach. Def Sci J 60(3):290–301

    Article  Google Scholar 

  14. Chaudhuri D, Samal A (2008) An automatic bridge detection technique for multispectral image. IEEE Trans Geosci Remote Sens 46(9):2720–2727

    Article  Google Scholar 

  15. Chaudhuri D, Mishra A, Anand SK, Gohri G (2001) Optimal splitting technique for remote sensing satellite imagery data. Vis Inf Process Conf SPIE’s Int Symp Aero Sense 4388:79–88

    Google Scholar 

  16. Chaudhuri D, Agrawal A, Mishra A, Gohri V, Agarwal RC, Samal A (2012) A statistical based approach for automatic detection of ocean disturbance features from SAR images. IEEE J Selected Top Appl Earth Observ Remote Sens 5(4):1231–1242

    Article  Google Scholar 

  17. Chaudhuri D, Kushwaha NK, Samal A (2012) Semi-automated road detection from high resolution satellite images by directional morphological enhancement and segmentation techniques. IEEE J Select Topics Appl Earth Observ Remote Sens 5(5):1538–1544

    Article  Google Scholar 

  18. Costa L, da F, Cesar R (2001) Shape analysis and classification: theory and practice. CRC Press, Boca Raton

    MATH  Google Scholar 

  19. Cover TM, Thomas J (1991) Elements of information theory. Wiley

  20. Dong L, Yu G, Ogunbona P, Li W (2008) An efficient iteration algorithm for image thresholding. Pattern Recogn Lett 29(9):1311–1316

    Article  Google Scholar 

  21. Feldman D, Crutchfield J (2003) Structural information in two-dimensional patterns: entropy convergence and excess entropy. Phys Rev E 67

  22. Feng D, Wenkanga S, Liangzhou C, Yong D, Zhenfu Z (2005) Infrared image segmentation with 2-D maximum entropy method based on particle swarm optimization (PSO). Pattern Recogn Lett 26:597–603

    Article  Google Scholar 

  23. Fexias M, Bardera A, Rigau J, Xu Q, Sbert M (2014) Information theory tools and image processing synthesis. Lectures on computer graphics and animation. Morgan and Claypool Publishers. ISBN-13:978-1627053617

  24. Gonzalez RC, Woods RE (2001) Digital image processing. Pearson Education, Delhi

    Google Scholar 

  25. Guo C, Li H (2007) Multilevel thresholding method for image segmentation based on an adaptive particle swarm optimization algorithm. Springerlink Lect Notes Comput Sci 4830:1611–3349

    Google Scholar 

  26. Haralick RM, Shanmugan K, Dinstein I (1973) Textural features for image classification. IEEE Trans Syst Man Cybern 3:610–621

    Article  Google Scholar 

  27. Horowitz SL, Pavlidis T (1974) Picture segmentation by directed split and merge procedure. In: Proceedings of 2nd international joint conference on pattern recognition, pp 424–33

  28. Huang DY, Wang CH (2009) Optimal multilevel thresholding using a two stage Otsu optimization approach. Pattern Recogn Lett 30(3):275–284

    Article  Google Scholar 

  29. Huang DY, Lin TW, Hu WC (2011) Automatic multilevel thresholding based on two stage Otsu’s method with cluster determination by valley estimation. Int J Innov Comput Inf Control 7(10):5631–5644

    Google Scholar 

  30. Jalba AC, Roerdink JBTM (2003) Automatic segmentation of diatom images. In: Proceedings of 10th International conference on computer analysis of images and patterns (CAIP 2003), pp 329–36

  31. Jeon J, Manmatha R (2004) Using maximum entropy for automatic image annotation. Springerlink Lect Notes Comput Scis (LNCS) 3115:24–32

    Article  Google Scholar 

  32. Kapur J, Sahoo P, Wong A (1985) A new method for gray-level picture thresholding using the entropy of the histogram. Comput Vis Graph Image Process 29(3):273–285

    Article  Google Scholar 

  33. Kuijper A, Florack L, Viergever M (2003) Scale space hierarchy. J Math Imag Vis 18:169–189

    Article  MathSciNet  MATH  Google Scholar 

  34. Kushwaha NK, Chaudhuri D, Singh MP (2013) Automatic bright circular type oil tank detection using remote sensing images. Def Sci J 63(3):298–304

    Article  Google Scholar 

  35. Lee SU, Chung SY, Park RH (1990) A comparative performance study of several global thresholding techniques for segmentation. Comput Vis Graph Image Process 52:171–190

    Article  Google Scholar 

  36. Lei X, Fu A (2009) 2-d maximum-entropy thresholding image segmentation method based on second-order oscillating PSO.ICNC:161–165

  37. Liao PS, Chen TS, Chung PC (2001) A fast algorithm for multi-level thresholding. J Inf Sci Eng 17(5):713–727

    Google Scholar 

  38. Liscano R, Wong AKC (1995) A study into entropy-based thresholding for image edge detection. Vis Interf 95:38–44

    Google Scholar 

  39. Liu L et al. (2003) An entropy based segmentation algorithm for computer-generated document images. IEEE Image Process 541–544

  40. Nakagawa Y, Rosenfeld A (1979) Some experiments on variable thresholding. Pattern Recogn 11:191–04

    Article  Google Scholar 

  41. Navon E, Miller O, Averbuch A (2005) Color image segmentation based on adaptive local thresholds. Image Vis Comput 23:69–85

    Article  Google Scholar 

  42. Noble J, Boukerroui D (2006) Ultrasound image segmentation. A survey. IEEE Trans Med Imag 25:987–010

    Article  Google Scholar 

  43. Otsu NA (1979) Threshold selection method from gray level histograms. IEEE Trans Syst Man Cybern 9:62–66

    Article  Google Scholar 

  44. Pal N, Pal SK (1991) Entropy–a new definition and its applications. IEEE Trans Syst. Man Cybern 21:5

    Article  MathSciNet  MATH  Google Scholar 

  45. Perez A, Gonzalez RC (1987) An iterative thresholding algorithm for image segmentation. IEEE Trans Pattern Anal Mach Intell 9:742–51

    Article  Google Scholar 

  46. Pun T (1980) A new method for gray-level picture thresholding using the entropy of the histogram. Signal Process 2:223–237

    Article  Google Scholar 

  47. Renyi A S (1961) On measures of entropy and information. Fourth Berkeley Symp 1:547–561

    MathSciNet  MATH  Google Scholar 

  48. Rigau J, Feixas M, Sbert M, Bardera A, Boada I (2004) Medical image segmentation based on mutual information maximization. In: Lecture notes in computer science (MICCAI 2004). Rennes-Saint Malo France pp 135–142

  49. Sadek S et al (2009) A new approach to image segmentation via fuzzification of Rènyi entropy of generalized distributions. World Acad Sci Eng Technol 598–603

  50. Sahoo PK, Soltani S, Wong AKC, Chen YC (1988) A survey of thresholding techniques. Comput Vis Graph Image Process 41:233–260

    Article  Google Scholar 

  51. Shannon CE (1948) A mathematical theory of communication. Bell Syst Tech J 379–423:379–423

    Article  MathSciNet  MATH  Google Scholar 

  52. Shin YS (2008) Method for finding optimal threshold for image segmentation. US Patent Issued No. 10817551

  53. Suresh Manic K, Krishna Priya R, Rajinikanth V (2016) Image multithresholding based on Kapur/Tsallis entropy and firefly algorithm. Indian J Sci Technol 9(12):1–6

    Google Scholar 

  54. Sushil K et al (2012) 2D maximum entropy method for image thresholding converge with differential evolution. Adv Mech Eng Appl(AMEA) 2(3):189–192

    Google Scholar 

  55. Taxt T, Flynn PJ, Jain AK (1989) Segmentation of document images. IEEE Trans Pattern Anal Mach Intell 11(12):1322–329

    Article  Google Scholar 

  56. Tsallis C, Abe S, Okamoto Y (2001) Non extensive statistical mechanics and its applications. Springer Lecture notes in Physics

  57. Vennila K, Thamizhmaran K (2017) Implementation of multilevel thresholding on image using firefly algorithm. Int J Adv Res Comput Sci 8(3):373–378

    Google Scholar 

  58. Yanowitz SD, Bruckstein AM (1996) A new method for image segmentation. Comp Vis Graph Image Proc 46:82–95

    Article  Google Scholar 

  59. Zhang R, Liu J (2006) Underwater image segmentation with maximum entropy based on particle swarm optimization (PSO). In: First international multi-symposiums on computer and computational sciences (IMSCCS’06), vol 2, pp 360–636

  60. Zhao W, Chellappa R, Phillips P, Rosenfeld A (2003) Face recognition: a literature survey. ACM Comput Surv (CSUR) 35:399–58

    Article  Google Scholar 

Download references

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Authors and Affiliations

  1. Department of Information Technology, DIT University, Dehradun, Uttarakhand, India

    Kuntal Chowdhury

  2. DRDO Integration Centre, Panagarh, West Bengal, India

    Debasis Chaudhuri

  3. Department of Computer Science and Engineering, Indian Institute Of Technology (Indian School Of Mines) [IIT(ISM)], Dhanbad, Jharkhand, India

    Arup Kumar Pal

Authors
  1. Kuntal Chowdhury
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  2. Debasis Chaudhuri
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  3. Arup Kumar Pal
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Correspondence to Kuntal Chowdhury.

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Chowdhury, K., Chaudhuri, D. & Pal, A.K. A new image segmentation technique using bi-entropy function minimization. Multimed Tools Appl 77, 20889–20915 (2018). https://doi.org/10.1007/s11042-017-5429-8

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