Skip to main content

Advertisement

SpringerLink
Log in

Breast lesion detection from MRI images using quasi-oppositional slime mould algorithm

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

In recent years cancer on breast in women has increased rapidly worldwide. Therefore, the automatic segmentation of breast Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) has been exhaustively investigated since the proper use of methods permits the diagnosis and identification of diseases. Radiologists accept that breast fat-suppressed DCE-MRI evaluation for lesion detection and segmentation, which optimization algorithm via multi-level thresholding, is essential to differentiate breast lesions from other tissue types in DCE-MRI. This article proposes a breast DCE-MRI segmentation method using a multilevel thresholding technique based on enhanced Slime Mould Algorithm (SMA). The anisotropic diffusion filter is used to denoise MR images first. The preprocessing step then corrects intensity inhomogeneities. The suggested SMAQOBL algorithm is used to segment preprocessed MR images. Next, we developed the enhanced SMA by incorporating the Quasi Opposition-based Learning (QOBL) mechanism in it. This algorithm is used to find optimal threshold values through the maximization of Shannon entropy. Throughout this article, the proposed algorithm has been termed SMAQOBL. Finally, the segmented lesions are accurately localized in MR images. The proposed method is evaluated using 200 sagittal T2-weighted fat-suppressed DCE-MRI images of 40 patients. The SMAQOBL is compared with SMA, Dragonfly Optimization (DA), Grasshopper Optimization Algorithm (GOA), Particle Swarm Optimizer (PSO), Multi-Verse Optimization (MVO), Conventional Markov Random Field (CMRF), Hidden Markov Random Field (HMRF), and Improved Markov Random Field (IMRF). The best-achieved results of the proposed method in terms of accuracy is 99.94%, sensitivity is 99.86% and Dice Similarity Coefficient (DSC) is 98.41%. Evaluating the proposed method achieves an mean accuracy of 99.36%, a mean sensitivity of 95.83%, and mean DSC of 92.19%. We have analyzed the results using a one-way ANOVA test with posthoc Tukey-HSD test and Wilcoxon Signed Rank Test with Bonferroni correction. Furthermore, we have also analyzed the overall performance using Multi-Criteria Decision Making based on sensitivity, accuracy, specificity, Geometric-Mean, F-measure, DSC, and False Positive Rate (FPR). The proposed methods outperform other compared methods, according to both quantitative and qualitative outcomes.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Algorithm 1
Fig. 3
Algorithm 2
Fig. 4
Algorithm 3
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21
Fig. 22

Similar content being viewed by others

Availability of data and material

Not Applicable.

Code availability

Not Applicable.

References

  1. Agner SC, Xu J, Rosen M, Karthigeyan S, Englander S, Madabhushi A (2011) Spectral embedding based active contour: application to breast lesion segmentation on dce-mri. Proc. SPIE 7963, Medical Imaging 2011: Computer-Aided Diagnosis (796305), pp 280–290

  2. Agliozzo S, Luca MD, Bracco C, Vignati A, Giannini V, Martincich L, Carbonaro LA, Bert A, Sardanelli F, Regge D (2012) Computer-aided diagnosis for dynamic contrast enhances breast mri of mass-like lesions using a multiparametric model combining a selection of morphological, kinetic, and spatiotemporal features. Med Phys 34:1704–1715

    Google Scholar 

  3. AlQoud A, Jaffar M A (2016) Hybrid gabor based local binary patterns texture features for classification of breast mammograms. International Journal of Computer Science and Network Security 16(4):16–21

    Google Scholar 

  4. Anscombe F (1948) The Validity of comparative experiments. J R Stat Soc 111(3):181–211

    MathSciNet  Google Scholar 

  5. Arbach L, Stolpen A, Reinhardt JM (2004) Classification of Breast MRI Lesions using a Backpropagation Neural Network. In 2004 2nd IEEE international symposium on biomedical imaging: Macro to Nano (IEEE Cat No.04EX821 2:253–256

    Google Scholar 

  6. Arjmand A, Meshgini S, Afrouzian R, Farzamnia A (2019) Breast Tumor Segmentation Using K-Means Clustering and Cuckoo Search Optimization. In: 2019 9th International conference on computer and knowledge engineering (ICCKE) 305-308. https://doi.org/10.1109/iccke48569.2019.8964794

  7. Azmi R, Norozi N (2011) A new markov random field segmentation method for breast lesion segmentation in mr images. Journal of Medical Signals Sensors 1:156–164

    Google Scholar 

  8. Balafar MA, Ramli AR, Mashohor S (2010) A new method for mr grayscale inhomogeneity correction. Artif Intell Rev 34:195–204

    Google Scholar 

  9. Behrens S, Laue H, Althaus M, Boehler T, Kuemmerlen B, Hahn HK, Peitgen HO (2007) Computer assistance for mr based diagnosis of breast cancer: present and future challenges. Comput Med Imaging Graph 31:236–247

    Google Scholar 

  10. Benjelloun M, Adoui ME, Larhmam MA, Mahmoudi SA (2018) Auto-mated breast tumor segmentation in DCE-MRI using deep learning. In: 4th International Conference on Cloud Computing Technologies and Applications (Cloudtech). https://doi.org/10.1109/CloudTech.2018.8713352

  11. Bergstra J, Bengio Y (2012) Random search for hyper-parameter optimization. J Mach Learn Res 13:281–305

    MathSciNet  MATH  Google Scholar 

  12. Bohare MD, Cheeran AN, Sarode VG (2011) Analysis of breast mri images using wavelets for detection of cancer. IJCA Special Issue on Electronics. Inf Commun Eng 4:1–3

    Google Scholar 

  13. Boukerroui D, Basset O, Guerin N, Baskurt A (1998) Multiresolution texture based adaptive clustering algorithm for breast lesion segmentation. Eur J Ultrasound 8:135–144

    Google Scholar 

  14. Bray F, Ren JS, Masuyer E (2013) Estimates of global cancer prevalence for 27 sites in the adult population in 2008. International Journal Cancer 132(5):1133–1145

    Google Scholar 

  15. Brown S, Tauler R, Walczak B (2020) Comprehensive Chemometrics- Chemical and Biochemical Data Analysis. 2nd Edition

  16. Chatzis SP, Tsechpenakis G (2010) The infinite hidden markov random field model. IEEE Transactions On Neural Networks 21(6):1004–1014

    Google Scholar 

  17. Clark K, Vendt B, Smith K, Freymann J, Kirby J, Koppel P, Moore S, Phillips S, Maffitt D, Pringle M, Tarbox L, Prior F (2013) The cancer imaging archive: Maintaining and operating a public information repository. Jounal Digit Imaging 26(6):1045–1057

    Google Scholar 

  18. Clerc M (1999) The swarm and the queen: Towards a deterministic and adaptive particle swarm optimization. In: Proceedings of the 1999 congress on evolutionary computation-CEC99 I: pp 1951–1957

  19. Cross GR, Jain AK (1983) Markov random field texture models. IEEE Transactions On Pattern Analysis And Machine Intelligence 5(1):25–39

    Google Scholar 

  20. Derrac J, Garcia S, Molina D (2001) A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms. Swarm and Evolutionary Springer Nature, 2021. LATEX template 40 Breast DCE-MRI segmentation using SMAQOBL Computation 1:3–18

    Google Scholar 

  21. Dhane DM, Maity M, Achar A, Bar C, Chakraborty C (2015) Selection of optimal denoising filter using quality assessment for potentially lethal optical wound images. Procedia Comput Sci 58:438–446

    Google Scholar 

  22. Eltoukhy MM, Faye I, Samir BB (2010) Breast cancer diagnosis in digital mammogram using multiscale curvelet transform. Comput Med Imaging Graph 34(4):269–276

    Google Scholar 

  23. Engelbrecht AP (2005) Fundamentals of Computational Swarm Intelligence, John Wiley Sons Ltd

  24. Eskandari P, Baradaran Shokouhi S (2021) Automated atlas-based segmentation of breast region in 3-D magnetic resonance imaging (MRI) using FCM method. Mapta Journal of Electrical and Computer Engineering Springer Nature 2021 LATEX template 42 Breast DCE-MRI segmentation using SMAQOBL (MJECE) 3(1):28–34. https://doi.org/10.33544/mjece.v3i1.175

    Article  Google Scholar 

  25. Ferlay J, Soerjomataram I, Ervik M (2013) Globocan 2012 v1.0, cancer incidence and mortality worldwide: Iarc cancer base. GLOBOCAN

  26. Ha W, Vahedi Z (2021) Automatic breast tumor diagnosis in mri based on a hybrid cnn and feature-based method using improved deer hunting opti- mization algorithm. Comput Intell Neurosci 2021(3):1–11. https://doi.org/10.1155/2021/5396327

    Article  Google Scholar 

  27. Hauth EA, Stockamp C, Maderwald S, Muhler A, Kimmig R, Jaeger H, Barkhausen J, Forsting M (2006) Evaluation of the three-time-point method for diagnosis of breast lesions in contrast-enhanced mr mammography. Clinic Imaging 30:160–165

    Google Scholar 

  28. Huynh-Thu Q, Ghanbari M (2008) Scope of validity of PSNR in image/video quality assessment. Electron Lett 44:800–801

    Google Scholar 

  29. Hommel G (1988) A stagewise rejective multiple test procedure based on a modified bonferroni test. Biometroka 75(2):383–386

    MATH  Google Scholar 

  30. Jiao Z, Gao X, Wang Y, Li J (2016) A deep feature based framework for breast masses classification. Computer-Aided Diagnosis in Medical Imaging 197:221–231

    Google Scholar 

  31. Kadry S, Damasevicius R, Taniar D, Rajinikanth V, Lawal I A (2021) Extraction of Tumour in Breast MRI using Joint Thresholding and Seg- mentation – A Study, Seventh International conference on Bio Signals, Images, and Instrumentation (ICBSII). https://doi.org/10.1109/ICBSII51839.2021.9445152

  32. Karthiga R, Narasimhan K (2018) Automated diagnosis of breast cancer using wavelet based entropy features. Second international conference on electronics, Communication and Aerospace Technology, pp 274–279. https://doi.org/10.1109/ICECA.2018.8474739

  33. Kashyap KL, Bajpai MK, Khanna P (2015) Breast cancer detection in digital mammograms. In: 2015 IEEE international conference on imaging systems and techniques (IST) pp 1-6

  34. Keyvanfard F, Shoorehdeli MA, Teshnehlab M, Nie K, Su M-Y (2013) Specificity enhancement in classification of breast mri lesion based on multi-classifier. Neural Comput & Applic 22(S1):35–45

    Google Scholar 

  35. Krishnaveni A, Shankar R, Duraisamy S (2021) Versatile duck traveler optimization algorithm using triple segmentation methods for Springer Nature 2021 LATEX template Breast DCE-MRI segmentation using SMAQOBL 43 mammogram image segmentation to improving accuracy. https://doi.org/10.2139/ssrn.3803814

  36. Kumar M, Mehta KK (2011) A texture based tumor detection and automatic segmentation using seeded region growing method. Int J Comp Tech Appl 2:855–859

    Google Scholar 

  37. Levman J, Leung T, Causer P, Plewes D, Martel AL (2008) Classification of dynamic contrast-enhanced magnetic resonance breast lesions by support vector machines. IEEE Trans Med Imaging 27(5):688–696

    Google Scholar 

  38. Li S, Chen H, Wang M, Heidari AA, Mirjalili S (2020) Slime mould algorithm: a new method for stochastic optimization. Futur Gener Comput Syst 111:300–323

    Google Scholar 

  39. Lingle W, Erickson BJ, Zuley ML, Jarosz R, Bonaccio E (2007) Filippini J, Radiology Data from the Cancer Genome Atlas Breast Invasive Carcinoma Collection [TCGA-BRCA], Gruszauskas N

  40. Mann RM, Kuhl CK, Kinkel K, Boetes C (2008) Breast MRI: Guidelines from the european society of breast imaging. Eur Radiol 18:1307–1318

    Google Scholar 

  41. ME GM, Subashini MM (2019) Medical imaging with intelligent systems: a review Sangaiah, A.K. (ed.) Deep Learning and Parallel Computing Environment for Bioengineering Systems, pp 53–73. Academic Press Chap 4. https://doi.org/10.1016/B978-0-12-816718-2.00011-7

  42. Mirjalili S (2016) Dragonfly algorithm: a aew meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems. Neural Comput and Applic 27:1053–1073

    Google Scholar 

  43. Mirjalili S, Mirjalili SM, Hatamlou A (2016) Multi-verse optimizer: a nature-inspired algorithm for global optimization. Natural Computing and Applications 27:495–513

    Google Scholar 

  44. Mirjalili SZ, Mirjalili S, Saremi S, Faris H, Aljarah I (2018) Grasshopper optimization algorithm for multi-objective optimization problems. Appl Intell 48:805–820

    Google Scholar 

  45. Mohan J, Krishnavenib V, Guo Y (2014) A survey on the magnetic resonance image denoising methods. Biomed Signal Process Control 9:56–69

    Google Scholar 

  46. Mustra M, Grgic M (2013) Robust automatic breast and pectoral mus- cle segmentation from scanned mammograms. Signal processing 93 (10):2817–2827

    Google Scholar 

  47. Naidu MSR, Kumar PR, Chiranjeevi K (2018) Shannon and Fuzzy entropy based evolutionary image thresholding for image segmentation. Alex Eng J 57:1643–1655

    Google Scholar 

  48. Nie K, Chen J-H, Chan S, Chau M-K I, Yu HJ, Bahri S, Tseng T, Nalcioglu O, Su M-Y (2008) Development of a quantitative method for analysis of breast density based on three-dimensional breast mri. Med Phys 35:5253–5262

    Google Scholar 

  49. Oliver A, Freixenet J, Marti J, Perez E, Pont J, Denton ERE, Zwiggelaar R (2010) A review of automatic mass detection and segmentation in mammographic images. Med Image Anal 14:87–110

    Google Scholar 

  50. Patra DK, Si T, Mondal S, Mukherjee P (2021) Breast DCE-MRI segmentation for lesion detection by multi-level thresholding using student psychological based optimization, biomedical signal processing and control 69(102925). https://doi.org/10.1016/j.bspc

  51. Patra DK, Mondal S, Mukherjee P (2021) Grammatical fireworks algorithm method for breast lesion segmentation in DCE-MRI. International Journal of Innovative Technology and Exploring Engineering 10(7):170–182. https://doi.org/10.35940/ijitee.G9054.0510721

    Article  Google Scholar 

  52. Perona P, Malik J (1990) Scale-space and edge detection using Anisotropic Diffussion. IEEE Trans. Pattern Anal Mach Intell 12(7):629–639

    Google Scholar 

  53. Piantadosi G, Marrone S, Galli A, Sansone M, Sansone C (2019) DCE-MRI Breast Lesions Segmentation with a 3TP U-Net Deep Convolutional Neural Network. In: 2019 IEEE 32nd International Symposium on Computer-Based Medical Systems (CBMS)

  54. Pizer SM, Johnston RE, Ericksen JP, Yankaskas BC, Muller KE (2002) Contrast-limited Adaptive Histogram Equalization, Speed and Effectiveness. In: 1990 Proceedings of the First Conference on Visualization in Biomedical Computing. https://doi.org/10.1109/VBC.1990.109340

  55. Rahnamayan S (2007) Quasi-Oppositional Differential Evolution. IEEE Congress on Evolutionary Computation. https://doi.org/10.1109/CEC.2007.4424748, Salama, MMA

  56. Samantaray L, Hembram S, Panda R (2020) A new Harris Hawks-Cuckoo search optimizer for multilevel thresholding of thermogram images. International Information and Engineering Technology Association 34:541–551. https://doi.org/10.18280/ria.340503

    Article  Google Scholar 

  57. Shannon C, Weaver W (1964) The mathematical theory of communication. Urbana Ill, University of illinois press

    MATH  Google Scholar 

  58. Suradi SH, Abdullah KA, Is MI (2021) Breast lesions detection using FADHECAL and multilevel otsu thresholding segmentation in digital mammograms. In: Badnjevic A, Gurbeta Pokvi CL (eds) Proceedings of the International Conference on Medical and Biological Engineering, CMBEBIH 2021, April 21–24, 2021, Mostar, Bosnia and Herzegovina. CMBEBIH 2021, vol 84. Springer. https://doi.org/10.1007/978-3-030-73909-685

  59. Shi J, Sahiner B, Chan HP, Ge J, Hadjiiski L, Helvie MA, Nees A, Wu YT, Wei J, Zhou C, Zhang Y, Cui J (2008) Characterization of mammographic masses based on level set segmentation with new image features and patient information. Med Phys 35:280–290

    Google Scholar 

  60. Si T, De A, Bhattacharjee AK (2015) Brain mri segmentation for tumor detection via entropy maximization using grammatical swarm. Interna- tional Journal of Wavelets Multiresolution and Information Processing 13(5):1–32

    MathSciNet  MATH  Google Scholar 

  61. Si T, De A, Bhattacharjee AK (2015) Brain mri segmentation for tumor detection via entropy maximization using grammatical swarm. Interna- tional Journal of Wavelets, Multiresolution and Information Processing 13(5). https://doi.org/10.1142/S0219691315500393

  62. Si T, Dutta R (2019) Partial opposition-based particle swarm optimizer in artificial neural network training for medical data classification. Int J Inf Technol Decis Mak 18(5):1717–1750

    Google Scholar 

  63. Si T, Miranda P, Galdino JV, Nascimento A (2021) Grammar-based automatic programming for medical data classification: an experimental study. Artif Intell Rev, 54(3). https://doi.org/10.1007/s10462-020-09949-9

  64. Thakran S, Chatterjee S, Singhal M, Gupta RK, Singh A (2018) Automatic outer and inner breast tissue segmentation using multi-parametric mri images of breast tumor patients. Plos One 13(1):e0190348. https://doi.org/10.1371/journal.pone.0190348

    Article  Google Scholar 

  65. Tharwat A (2018) Classification assessment methods. Applied Computing and Informatics 17:168–192

    Google Scholar 

  66. (2019) The Cancer Imaging Archive: TCGA-BRCA https://wiki.cancerimagingarchive.net/display/Public/TCGA-BRCA. Accessed 02 Mar 2019

  67. Trelea IC (2002) The particle swarm optimization algorithm: Convergence analysis and parameter selection. Inf Process Lett 85:317–325

    MathSciNet  MATH  Google Scholar 

  68. Tizhoosh HR (2005) Opposition-Based Learning: A New Scheme for Machine Intelligence, I. pp 695–701

  69. Triantaphyllou E (2000) Multi-Criteria Decision Making Methods: A Comparative Study 44, 3rd edn. Springer. https://doi.org/10.1007/978-1-4757-3157-6

  70. Tukey JW (1949) Comparing individual means in the analysis of variance. Biometrics 5(2):99–114

    MathSciNet  Google Scholar 

  71. Tuncay AH, Akduman I (2015) Realistic microwave breast models through t1-weighted 3-d mri data. IEEE Trans Biomed Eng 62(2):688–698

    Google Scholar 

  72. Wajid SK, Hussain A, Huang K (2018) Three-dimensional local energy-based shape histogram (3d-lesh)-based feature extraction– a novel tech- nique. Expert Syst Appl 112:388–440

    Google Scholar 

  73. Wang H, Zhang Q, Wang Y, Hu H (2018) Structured probabilistic pruning for convolutional neural network acceleration. In: Proceedings of the British Machine Vision Conference (BMVC) v3

  74. World Health Organization (WHO) (2021) Breast cancer. https://www.who.int/news-room/fact-sheets/detail/breast-cancer. Accessed 08 Feb 2021

  75. Wu Q, Salganicoff M, Krishnan A, Fussell DS, Markey MK (2006) Inter-active lesion segmentation on dynamic contrast enhanced breast mri using a markov model. Proceedings Volume 6144, Medical Imaging 2006: Image Processing; 61444M

  76. Xu X, Fu L, Chen Y, Larsson R, Zhang D, Suo S, Hua J, Zhao J (2018) Breast region segmentation being convolutional neural network in dynamic contrast enhanced MRI. 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society 750–753

  77. Yao J, Chen J, Chow C (2009) Breast tumor analysis in dynamic contrast enhanced mri using texture features and wavelet transform. IEEE J Sel Top Signal Process 3:94–100

    Google Scholar 

  78. Zhang H, Foo SW, Krishnan SM, Hua Thng C (2004) Computer aided detection of breast masses from digitized mammograms, IEEE International Workshop on Biomedical Circuits and Systems :1–4

Download references

Funding

Not Applicable.

Author information

Author notes

    Authors and Affiliations

    1. Department of Computer Science, Raja Narendra Lal Khan Women’s College (Autonomous), Midnapore, 721102, West Bengal, India

      Dipak Kumar Patra

    2. Department of Computer Science & Engineering, University of Engineering & Management, Jaipur, GURUKUL, Sikar Road (NH-11), Udaipuria Mod, Jaipur-303807, Rajasthan, India

      Tapas Si

    3. Department of Mathematics, Raja Narendra Lal Khan Women’s College (Autonomous), Midnapore, 721102, West Bengal, India

      Sukumar Mondal

    4. Department of Mathematics, Hijli College, Midnapore, 721306, West Bengal, India

      Prakash Mukherjee

    Authors
    1. Dipak Kumar Patra
      View author publications

      You can also search for this author in PubMed Google Scholar

    2. Tapas Si
      View author publications

      You can also search for this author in PubMed Google Scholar

    3. Sukumar Mondal
      View author publications

      You can also search for this author in PubMed Google Scholar

    4. Prakash Mukherjee
      View author publications

      You can also search for this author in PubMed Google Scholar

    Contributions

    Dipak Kumar Patra Conceptualization of this study, Methodology, Programming, Writing, and Editing. Tapas Si Conceptualization of this study, Methodology, Data collection, Programming, Writing - Original draft preparation, Review, Editing. Sukumar Mondal Conceptualization of this study, Writing, Review, Editing. Prakash Mukherjee Conceptualization of this study, Writing, Review, Editing.

    Corresponding author

    Correspondence to Dipak Kumar Patra.

    Ethics declarations

    Ethics approval

    Not Applicable.

    Consent to participate

    Not Applicable.

    Conflict of Interests/Competing interests

    Authors declared that there are no conflicts of interest.

    Additional information

    Publisher’s note

    Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

    Tapas Si, Sukumar Mondal and Prakash Mukherjee are contributed equally to this work.

    Rights and permissions

    Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

    Reprints and permissions

    About this article

    Check for updates. Verify currency and authenticity via CrossMark

    Cite this article

    Patra, D.K., Si, T., Mondal, S. et al. Breast lesion detection from MRI images using quasi-oppositional slime mould algorithm. Multimed Tools Appl 82, 30599–30641 (2023). https://doi.org/10.1007/s11042-023-14329-w

    Download citation

    • Received:

    • Revised:

    • Accepted:

    • Published:

    • Issue Date:

    • DOI: https://doi.org/10.1007/s11042-023-14329-w

    Keywords

    Search

    Navigation