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Chaotic multi verse improved Harris hawks optimization (CMV-IHHO) facilitated multiple level set model with an ideal energy active contour for an effective medical image segmentation

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Abstract

Nowadays, the contour models (CMs) are widely used in image segmentation. Among these CMs, the Chan and Vese model depending on level set is the current regional based model, considering the regularity of intensity in every region. If the contour is not initialized correctly, the conventional level set (LS) model frequently sticks in local minima. This is becoming more important in the medical images context. In this manuscript, a multi-level set model with an ideal energy active contour is proposed, which is anticipated to realize the performance of acceptable segmentation, regardless of the contour’s initial choice. The active contour models are utilized to identify an object outline from the image. The active CMs with energy based segmentation methods minimizes the energy related with active contour. This work makes the appropriate energy minimized difficult to solve by using meta-heuristic optimization algorithm and makes a proficient execution of the approach by Chaotic Multi Verse Improved Harris Hawks Optimization (CMV-IHHO) technique. Here, the proposed approach is compared with six existing approaches. The existing methods such as DA, Symmetry Analysis, Fuzzy C-Means, Rough Fuzzy C-Means, K-Means Level Set, Random Forest, and Support vector machine method. The accuracy of the proposed method is 0.91%, 5.84%, 15.63%, 8.30%, 10.97%, 15.77%, and 5.14% better than the existing approaches. The sensitivity of the proposed method is 3.37%, 5.74%, 22.66%, 4.54%, 17.94%, 4.54% and 15% better than the existing methods.

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References

  1. Ahn E, Kim J, Bi L, Kumar A, Li C, Fulham M, Feng DD (2017) Saliency-based lesion segmentation via background detection in dermoscopic images. IEEE J Biomed Health Inform 21(6):1685–1693

    Article  Google Scholar 

  2. Amarapur B (2020) Computer-aided diagnosis applied to MRI images of brain tumor using cognition based modified level set and optimized ANN classifier. Multimed Tools Appl 79(5):3571–3599

    Google Scholar 

  3. Chartrand G, Cresson T, Chav R, Gotra A, Tang A, De Guise JA (2016) Liver segmentation on CT and MR using Laplacian mesh optimization. IEEE Trans Biomed Eng 64(9):2110–2121

    Article  Google Scholar 

  4. Chen H, Heidari AA, Chen H, Wang M, Pan Z, Gandomi AH (2020) Multi-population differential evolution-assisted Harris hawks optimization: framework and case studies. Futur Gener Comput Syst 111:175–198

    Article  Google Scholar 

  5. Choy SK, Lam SY, Yu KW, Lee WY, Leung KT (2017) Fuzzy model-based clustering and its application in image segmentation. Pattern Recogn 68:141–157

    Article  Google Scholar 

  6. Ewees AA, Abd Elaziz M (2020) Performance analysis of chaotic multi-verse Harris hawks optimization: a case study on solving engineering problems. Eng Appl Artif Intell 88:103370

    Article  Google Scholar 

  7. Gupta S, Deep K, Heidari A et al (2020) Opposition-based learning Harris hawks optimization with advanced transition rules: principles and analysis. Expert Syst Appl 158:113510. https://doi.org/10.1016/j.eswa.2020.113510

    Article  Google Scholar 

  8. Jia H, Lang C, Oliva D, Song W, Peng X (2019) Dynamic Harris hawks optimization with mutation mechanism for satellite image segmentation. Remote Sens 11(12):1421

    Article  Google Scholar 

  9. Jiang H, Piao S, Qadir MZ, Guo Q (2019) M-WRSF model for medical image segmentation. Electron Lett 55(7):386–389

    Article  Google Scholar 

  10. Jung JH, Szule J (2017) Automatic optimization method for segmentation and surface model generation in electron tomography. IEEE Life Sci Lett 3(2):5–8

    Article  Google Scholar 

  11. Kaur R, Juneja M, Mandal A (2018) A hybrid edge-based technique for segmentation of renal lesions in ct images. Multimed tools Appl. 1–21.

  12. Li D, Deng N, Chen X (2019) Level set medical image segmentation aided by cooperative quantum particle optimization with Lévy flights. Vibroengineering PROCEDIA 28:93–98

    Article  Google Scholar 

  13. Li X, Wang X, Dai Y (2018) Adaptive energy weight based active contour model for robust medical image segmentation. J Signal Process Syst 90(3):449–465

    Article  Google Scholar 

  14. Mandal D, Chatterjee A, Maitra M (2017) Particle swarm optimization based fast Chan-Vese algorithm for medical image segmentation. In Metaheuristics for medicine and biology. 49-74. Springer, Berlin, Heidelberg.

  15. Mythili S, Thiyagarajah K, Rajesh P, Shajin FH (2020) Ideal position and size selection of unified power flow controllers (UPFCs) to upgrade the dynamic stability of systems: an antlion optimiser and invasive weed optimisation algorithm. HKIE Trans 27(1):25–37

    Article  Google Scholar 

  16. Obaidullah SM, Halder C, Santosh K, Das N, Roy K (2018) Phdindic 11: page-level handwritten document image dataset of 11 official indic scripts for script identification. Multimed Tools Appl 77(2):1643–1678

    Article  Google Scholar 

  17. Rajesh P, Shajin F (2020) A multi-objective hybrid algorithm for planning electrical distribution system. Eur J Electrical Eng 22(4–5):224–509

    Article  Google Scholar 

  18. Ren S, Laub P, Lu Y, Naganawa M, Carson RE (2019) Atlas-based multiorgan segmentation for dynamic abdominal PET. IEEE Trans Radiation Plasma Med Sci 4(1):50–62

    Article  Google Scholar 

  19. Sayed GI, Darwish A, Hassanien AE (2019) Quantum multiverse optimization algorithm for optimization problems. Neural Comput Applic 31:1–8

    Google Scholar 

  20. Shajin F, Rajesh P (2020) Trusted secure geographic routing protocol: outsider attack detection in mobile ad hoc networks by adopting trusted secure geographic routing protocol Int J Pervasive Comput Commun

  21. Singh T (2020) A chaotic sequence-guided Harris hawks optimizer for data clustering. Neural Comput Applic 32:17789–17803

    Article  Google Scholar 

  22. Tan TY, Zhang L, Lim CP, Fielding B, Yu Y, Anderson E (2019) Evolving ensemble models for image segmentation using enhanced particle swarm optimization. IEEE Access 7:34004–34019

    Article  Google Scholar 

  23. Thota MK, Shajin FH, Rajesh P (2020) Survey on software defect prediction techniques. Int J Appl Sci Eng 17:331–344

    Google Scholar 

  24. Wang G, Li W, Zuluaga MA, Pratt R, Patel PA, Aertsen M, Doel T, David AL, Deprest J, Ourselin S, Vercauteren T (2018) Interactive medical image segmentation using deep learning with image-specific fine tuning. IEEE Trans Med Imaging 37(7):1562–1573

    Article  Google Scholar 

  25. Wang X, Li W, Zhang C, Lou W, Song R (2019) An adaptable active contour model for medical image segmentation based on region and edge information. Multimed Tools Appl 78(23):33921–33937

    Article  Google Scholar 

  26. Yu H, He F, Pan Y (2019) A novel segmentation model for medical images with intensity inhomogeneity based on adaptive perturbation. Multimed Tools Appl 78(9):11779–11798

    Article  Google Scholar 

  27. Zhang Z, Xie YM, Li Q, Zhou S (2020) A reaction–diffusion based level set method for image segmentation in three dimensions. Eng Appl Artif Intell 96:103998

    Article  Google Scholar 

  28. Zhou S, Wang J, Zhang M, Cai Q, Gong Y (2017) Correntropy-based level set method for medical image segmentation and bias correction. Neurocomputing. 234:216–229

    Article  Google Scholar 

  29. Zhou Z, Siddiquee MM, Tajbakhsh N, Liang J (2018) Unet++: a nested u-net architecture for medical image segmentation. InDeep learning in medical image analysis and multimodal learning for clinical decision support. 3-11. Springer, Cham.

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Correspondence to Rangu Srikanth.

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Srikanth, R., Bikshalu, K. Chaotic multi verse improved Harris hawks optimization (CMV-IHHO) facilitated multiple level set model with an ideal energy active contour for an effective medical image segmentation. Multimed Tools Appl 81, 20963–20992 (2022). https://doi.org/10.1007/s11042-022-12344-x

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