Skip to main content
SpringerLink
Log in

An Optimized MSER Using Bat Algorithm for Skin Lesion Detection

  • Conference paper
  • First Online:
Artificial Intelligence: Theories and Applications (ICAITA 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1769))

  • 274 Accesses

Abstract

Detecting regions of interest in skin lesion images is of great significance in dermatological image analysis. In this article, we present a novel approach for the skin lesion detection in melanoma images based on bat algorithm and maximally stable extremal regions. The purpose is to better localize the regions of interest. To evaluate the proposed approach, the detection process is tested on the skin lesion images (melanoma, nevus) from MED-NODE and Atlas (dermIS, dermQUST) databases. The obtained qualitative and statistical results demonstrate the superiority of the proposed detector.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 69.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 89.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

Notes

  1. 1.

    http://www.dermweb.com/photo_atlas/.

References

  1. Altan, A., Karasu, S.: Recognition of covid-19 disease from x-ray images by hybrid model consisting of 2d curvelet transform, chaotic salp swarm algorithm and deep learning technique. Chaos Solitons Fractals 140, 110071 (2020). https://doi.org/10.1016/j.chaos.2020.110071

    Article  MathSciNet  Google Scholar 

  2. Aurelia, J.E., Rustam, Z., Wibowo, V.V.P., Setiawan, Q.S.: Comparison between convolutional neural network and convolutional neural network-support vector machines as the classifier for colon cancer. In: 2020 International Conference on Decision Aid Sciences and Application (DASA), pp. 812–816 (2020). https://doi.org/10.1109/DASA51403.2020.9317103

  3. Bay, H., Ess, A., Tuytelaars, T., Van Gool, L.: Speeded-up robust features (surf). Comput. Vis. Image Underst. 110(3), 346–359 (2008). https://doi.org/10.1016/j.cviu.2007.09.014. Similarity Matching in Computer Vision and Multimedia

  4. Calonder, M., Lepetit, V., Strecha, C., Fua, P.: BRIEF: binary robust independent elementary features. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010. LNCS, vol. 6314, pp. 778–792. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-15561-1_56

    Chapter  Google Scholar 

  5. Campilho, A., Karray, F., Wang, Z. (eds.): ICIAR 2020. LNCS, vol. 12132. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-50516-5

    Book  Google Scholar 

  6. Davis, J.E., Bednar, A.E., Goodin, C.T.: Optimizing maximally stable extremal regions (mser) parameters using the particle swarm optimization algorithm. Technical report ERDC (2019)

    Google Scholar 

  7. Giotis, I., Molders, N., Land, S., Biehl, M., Jonkman, M.F., Petkov, N.: Med-node: a computer-assisted melanoma diagnosis system using non-dermoscopic images. Expert Syst. Appl. 42(19), 6578–6585 (2015)

    Article  Google Scholar 

  8. Gupta, D., Arora, J., Agrawal, U., Khanna, A., de Albuquerque, V.H.C.: Optimized binary bat algorithm for classification of white blood cells. Measurement 143, 180–190 (2019). https://doi.org/10.1016/j.measurement.2019.01.002

    Article  Google Scholar 

  9. Harris, C., Stephens, M.: A combined corner and edge detector. In: Proceedings of the Alvey Vision Conference, pp. 23.1–23.6. Alvety Vision Club (1988). https://doi.org/10.5244/C.2.23

  10. Hassan, S.A., Sayed, M.S., Abdalla, M.I., Rashwan, M.A.: Detection of breast cancer mass using MSER detector and features matching. Multimedia Tools Appl. 78(14), 20239–20262 (2019). https://doi.org/10.1007/s11042-019-7358-1

    Article  Google Scholar 

  11. Heraguemi, K.E., Kamel, N., Drias, H.: Multi-objective bat algorithm for mining numerical association rules. Int. J.Bio-Inspired Comput. 11(4), 239–248 (2018). https://doi.org/10.1504/IJBIC.2018.092797

    Article  Google Scholar 

  12. Joshi, K., Patel, M.I.: Recent advances in local feature detector and descriptor: a literature survey. Int. J. Multimedia Inf. Retrieval 9(4), 231–247 (2020). https://doi.org/10.1007/s13735-020-00200-3

    Article  Google Scholar 

  13. Jyotiyana, P., Maheshwari, S.: Maximal stable extremal region extraction of MRI tumor images using successive Otsu algorithm. In: Fong, S., Akashe, S., Mahalle, P.N. (eds.) Information and Communication Technology for Competitive Strategies. LNNS, vol. 40, pp. 687–700. Springer, Singapore (2019). https://doi.org/10.1007/978-981-13-0586-3_67

    Chapter  Google Scholar 

  14. Karthika Devi, R., Banumathi, A., Sangavi, G., Sheik Dawood, M.: A novel region based thresholding for dental cyst extraction in digital dental X-ray images. In: Smys, S., Iliyasu, A.M., Bestak, R., Shi, F. (eds.) ICCVBIC 2018, pp. 1633–1640. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-41862-5_167

    Chapter  Google Scholar 

  15. Korotkov, K.: Automatic change detection in multiple pigmented skin lesions. TDX (Tesis Doctorals en Xarxa) (2014). https://dugi-doc.udg.edu/handle/10256/9276

  16. Lee, Y.W., Choi, J.W., Shin, E.H.: Machine learning model for predicting malaria using clinical information. Comput. Biol. Med. 129, 104151 (2021). https://doi.org/10.1016/j.compbiomed.2020.104151

    Article  Google Scholar 

  17. Li, H., Pan, Y., Zhao, J., Zhang, L.: Skin disease diagnosis with deep learning: a review. Neurocomputing 464, 364–393 (2021). https://doi.org/10.1016/j.neucom.2021.08.096

    Article  Google Scholar 

  18. Li, Y., Cui, X., Fan, J., Wang, T.: Global chaotic bat algorithm for feature selection. J. Supercomput. (2022). https://doi.org/10.1007/s11227-022-04606-0

  19. Lowe, D.: Object recognition from local scale-invariant features. In: Proceedings of the Seventh IEEE International Conference on Computer Vision, vol. 2, pp. 1150–1157 (1999). https://doi.org/10.1109/ICCV.1999.790410

  20. Matas, J., Chum, O., Urban, M., Pajdla, T.: Robust wide-baseline stereo from maximally stable extremal regions. Image Vis. Comput. 22(10), 761–767 (2004). https://doi.org/10.1016/j.imavis.2004.02.006. British Machine Vision Computing 2002

  21. Mikolajczyk, K., Schmid, C.: An affine invariant interest point detector. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, vol. 2350, pp. 128–142. Springer, Heidelberg (2002). https://doi.org/10.1007/3-540-47969-4_9

    Chapter  Google Scholar 

  22. Oltu, B., Güney, S., Dengiz, B., Ağıldere, M.: Automated tuberculosis detection using pre-trained CNN and SVM. In: 2021 44th International Conference on Telecommunications and Signal Processing (TSP), pp. 92–95 (2021). https://doi.org/10.1109/TSP52935.2021.9522644

  23. Ortiz-Toro, C., García-Pedrero, A., Lillo-Saavedra, M., Gonzalo-Martín, C.: Automatic detection of pneumonia in chest x-ray images using textural features. Comput. Biol. Med. 145, 105466 (2022). https://doi.org/10.1016/j.compbiomed.2022.105466

    Article  Google Scholar 

  24. Ravikumar, M., Rachana, P.G.: Study on different approaches for breast cancer detection: a review. SN Comput. Sci. 3(1), 1–6 (2021). https://doi.org/10.1007/s42979-021-00898-w

    Article  Google Scholar 

  25. Rosten, E., Drummond, T.: Machine learning for high-speed corner detection. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3951, pp. 430–443. Springer, Heidelberg (2006). https://doi.org/10.1007/11744023_34

    Chapter  Google Scholar 

  26. Rublee, E., Rabaud, V., Konolige, K., Bradski, G.: Orb: an efficient alternative to sift or surf. In: 2011 International Conference on Computer Vision, pp. 2564–2571 (2011). https://doi.org/10.1109/ICCV.2011.6126544

  27. Saha, S.K., Kar, R., Mandal, D., Ghoshal, S.P., Mukherjee, V.: A new design method using opposition-based bat algorithm for IIR system identification problem. Int. J. Bio-Inspired Comput. 5(2), 99–132 (2013). https://doi.org/10.1504/IJBIC.2013.053508

    Article  Google Scholar 

  28. Santosh, K., Dey, N., Antani, S., Guru, D.: Medical imaging: artificial intelligence, image recognition, and machine learning techniques. CRC Press LLC (2019)

    Google Scholar 

  29. Sengupta, S., Singh, A., Leopold, H.A., Gulati, T., Lakshminarayanan, V.: Ophthalmic diagnosis using deep learning with fundus images - a critical review. Artif. Intell. Med. 102, 101758 (2020). https://doi.org/10.1016/j.artmed.2019.101758

    Article  Google Scholar 

  30. Shareh, M.B., Bargh, S.H., Hosseinabadi, A.A.R., Slowik, A.: An improved bat optimization algorithm to solve the tasks scheduling problem in open shop. Neural Comput. Appl. 33(5), 1559–1573 (2020). https://doi.org/10.1007/s00521-020-05055-7

    Article  Google Scholar 

  31. Singh, A., Kharkar, N., Priyanka, P., Parvartikar, S.: Alzheimer’s disease detection using deep learning-CNN. In: Hu, Y.C., Tiwari, S., Trivedi, M.C., Mishra, K.K. (eds.) Ambient Communications and Computer Systems, pp. 529–537. Springer Nature Singapore, Singapore (2022). https://doi.org/10.1007/978-981-16-7952-0_50

    Chapter  Google Scholar 

  32. Smith, S.M., Brady, J.M.: Susan-a new approach to low level image processing. Int. J. Comput. Vision 23(1), 45–78 (1997). https://doi.org/10.1023/A:1007963824710

    Article  Google Scholar 

  33. Smucker, M.D., Allan, J., Carterette, B.: A comparison of statistical significance tests for information retrieval evaluation. In: Proceedings of the 16th ACM Conference on Conference on Information and Knowledge Management, pp. 623–632 (2007)

    Google Scholar 

  34. Tzutalin: Labelimg. Free Software: MIT License (2015). https://github.com/tzutalin/labelImg

  35. Yang, X.S.: A new metaheuristic bat-inspired algorithm. In: González, J.R., Pelta, D.A., Cruz, C., Terrazas, G., Krasnogor, N. (eds.) Nature Inspired Cooperative Strategies for Optimization (NICSO 2010), pp. 65–74. Springer, Berlin (2010). https://doi.org/10.1007/978-3-642-12538-6_6

    Chapter  Google Scholar 

  36. Yang, X.S., He, X.: Bat algorithm: literature review and applications. Int. J. Bio-inspired Comput. 5(3), 141–149 (2013)

    Article  Google Scholar 

  37. Zhu, H., Sheng, J., Zhang, F., Zhou, J., Wang, J.: Improved maximally stable extremal regions based method for the segmentation of ultrasonic liver images. Multimedia Tools Appl. 75(18), 10979–10997 (2015). https://doi.org/10.1007/s11042-015-2822-z

    Article  Google Scholar 

  38. Zhu, L.F., Wang, J.S., Wang, H.Y., Guo, S.S., Guo, M.W., Xie, W.: Data clustering method based on improved bat algorithm with six convergence factors and local search operators. IEEE Access 8, 80536–80560 (2020). https://doi.org/10.1109/ACCESS.2020.2991091

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Faculty of Sciences, Department of Computer Science, University of Algiers, Algiers, Algeria

    Khadidja Belattar, Maroua Ridane & Loubna Ahmed Chaouch

  2. LRIA, USTHB, Algiers, Algeria

    Mohamed Ait Mehdi

Authors
  1. Khadidja Belattar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mohamed Ait Mehdi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Maroua Ridane
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Loubna Ahmed Chaouch
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Khadidja Belattar .

Editor information

Editors and Affiliations

  1. University of Mascara, Mascara, Algeria

    Mohammed Salem

  2. University of Granada, Granada, Spain

    Juan Julián Merelo

  3. Université Paris-Est Créteil, Créteil, France

    Patrick Siarry

  4. University of Mascara, Mascara, Algeria

    Rochdi Bachir Bouiadjra

  5. University of Mascara, Mascara, Algeria

    Mohamed Debakla

  6. University of Mascara, Mascara, Algeria

    Fatima Debbat

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Belattar, K., Ait Mehdi, M., Ridane, M., Ahmed Chaouch, L. (2023). An Optimized MSER Using Bat Algorithm for Skin Lesion Detection. In: Salem, M., Merelo, J.J., Siarry, P., Bachir Bouiadjra, R., Debakla, M., Debbat, F. (eds) Artificial Intelligence: Theories and Applications. ICAITA 2022. Communications in Computer and Information Science, vol 1769. Springer, Cham. https://doi.org/10.1007/978-3-031-28540-0_7

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-28540-0_7

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-28539-4

  • Online ISBN: 978-3-031-28540-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation