Skip to main content
SpringerLink
Log in

Medical image segmentation using an optimized three-tier quantum convolutional neural network trained with hybrid optimization approach

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

Abstract

Medical image segmentation is a crucial task in medical image analysis. The proposed method for medical image segmentation involves several steps. First, pre-processing techniques such as Gaussian filtering and contrast stretching are applied to the input image. Next, a region of interest (ROI) is identified from the pre-processed image using an optimized mask RCNN, with the weight function of the RCNN optimized via a new hybrid optimization algorithm- Cuckoo-Spider Optimization, combining Cuckoo Search (CS) and Social Spider Optimization (SSO). After ROI identification, feature extraction is performed, including texture features such as Gray-Level Run Length Matrix (GLRLM), Local rotation invariant Texture Pattern (LrTP), and an Augmented Local Directional Pattern (A-LDP) proposed in this work. Additionally, shape features such as area and perimeter, and color features such as color histogram are extracted. Finally, an optimized three-tier quantum convolutional neural network (O-TT-QCNN) is proposed for segmentation, which can handle complex and heterogeneous medical images. The experimental results demonstrate that the proposed method achieves state-of-the-art performance on several benchmark datasets.

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
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

Data availability

All the data is collected from the simulation reports of the software and tools used by the authors. Authors are working on implementing the same using real world data with appropriate permissions.

References

  1. Gu Z, Cheng J, Fu H, Zhou K, Hao H, Zhao Y, Zhang T, Gao S, Liu J (2019) Ce-net: Context encoder network for 2d medical image segmentation. IEEE Trans Med Imaging 38(10):2281–2292

    Article  Google Scholar 

  2. Hesamian MH, Jia W, He X, Kennedy P (2019) Deep learning techniques for medical image segmentation: achievements and challenges. J Digit Imaging 32:582–596

    Article  Google Scholar 

  3. Vardhana M, Arunkumar N, Lasrado S, Abdulhay E, Ramirez-Gonzalez G (2018) Convolutional neural network for bio-medical image segmentation with hardware acceleration. Cogn Syst Res 50:10–14

    Article  Google Scholar 

  4. Minnema J, van Eijnatten M, Kouw W, Diblen F, Mendrik A, Wolff J (2018) CT image segmentation of bone for medical additive manufacturing using a convolutional neural network. Comput Biol Med 103:130–139

    Article  Google Scholar 

  5. Nguyen TP, Choi S, Park SJ, Park SH, Yoon J (2021) Inspecting method for defective casting products with convolutional neural network (CNN). Intl J Precision Eng Manuf Green Technol 8:583–594

    Article  Google Scholar 

  6. Sekaran K, Chandana P, Krishna NM, Kadry S (2020) Deep learning convolutional neural network (CNN) With Gaussian mixture model for predicting pancreatic cancer. Multimed Tools Appl 79(15-16):10233–10247

    Article  Google Scholar 

  7. Sultana F, Sufian A, Dutta P (2020) Evolution of image segmentation using deep convolutional neural network: A survey. Knowl-Based Syst 201:106062

    Article  Google Scholar 

  8. Baldeon-Calisto M, Lai-Yuen SK (2020) AdaResU-Net: Multiobjective adaptive convolutional neural network for medical image segmentation. Neurocomputing 392:325–340

    Article  Google Scholar 

  9. Sharma S, Saha AK, Majumder A, Nama S (2021) MPBOA-A novel hybrid butterfly optimization algorithm with symbiosis organisms search for global optimization and image segmentation. Multimed Tools Appl 80:12035–12076

    Article  Google Scholar 

  10. Zhang M, Jiang W, Zhou X, Xue Y, Chen S (2019) A hybrid biogeography-based optimization and fuzzy C-means algorithm for image segmentation. Soft Comput 23:2033–2046

    Article  Google Scholar 

  11. Ma J, Chen J, Ng M, Huang R, Li Y, Li C, Yang X, Martel AL (2021) Loss odyssey in medical image segmentation. Med Image Anal 71:102035

    Article  Google Scholar 

  12. Abd Elaziz M, Yousri D, Al-qaness MA, AbdelAty AM, Radwan AG, Ewees AA (2021) A Grunwald–Letnikov based Manta ray foraging optimizer for global optimization and image segmentation. Eng Appl Artif Intell 98:104105

    Article  Google Scholar 

  13. Ahmadi M, Kazemi K, Aarabi A, Niknam T, Helfroush MS (2019) Image segmentation using multilevel thresholding based on modified bird mating optimization. Multimed Tools Appl 78:23003–23027

    Article  Google Scholar 

  14. Yue X, Zhang H (2019) Improved hybrid bat algorithm with invasive weed and its application in image segmentation. Arab J Sci Eng 44:9221–9234

    Article  Google Scholar 

  15. Chouksey M, Jha RK, Sharma R (2020) A fast technique for image segmentation based on two meta-heuristic algorithms. Multimed Tools Appl 79(27-28):19075–19127

    Article  Google Scholar 

  16. Chen L, Bentley P, Mori K, Misawa K, Fujiwara M, Rueckert D (2018) DRINet for medical image segmentation. IEEE Trans Med Imaging 37(11):2453–2462

    Article  Google Scholar 

  17. 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 

  18. Jiang F, Grigorev A, Rho S, Tian Z, Fu Y, Jifara W, Adil K, Liu S (2018) Medical image semantic segmentation based on deep learning. Neural Comput & Applic 29:1257–1265

    Article  Google Scholar 

  19. Sourati J, Gholipour A, Dy JG, Tomas-Fernandez X, Kurugol S, Warfield SK (2019) Intelligent labeling based on fisher information for medical image segmentation using deep learning. IEEE Trans Med Imaging 38(11):2642–2653

    Article  Google Scholar 

  20. Karimi D, Salcudean SE (2019) Reducing the hausdorff distance in medical image segmentation with convolutional neural networks. IEEE Trans Med Imaging 39(2):499–513

    Article  Google Scholar 

  21. Feng-Ping A, Zhi-Wen L (2019) Medical image segmentation algorithm based on feedback mechanism convolutional neural network. Biomed Signal Process Control 53:101589

    Article  Google Scholar 

  22. Gu R, Wang G, Song T, Huang R, Aertsen M, Deprest J, Ourselin S, Vercauteren T, Zhang S (2020) CA-Net: Comprehensive attention convolutional neural networks for explainable medical image segmentation. IEEE Trans Med Imaging 40(2):699–711

    Article  Google Scholar 

  23. Feng S, Zhao H, Shi F, Cheng X, Wang M, Ma Y, Xiang D, Zhu W, Chen X (2020) CPFNet: Context pyramid fusion network for medical image segmentation. IEEE Trans Med Imaging 39(10):3008–3018

    Article  Google Scholar 

  24. Ma H, Zou Y, Liu PX (2021) MHSU-Net: A more versatile neural network for medical image segmentation. Comput Methods Prog Biomed 208:106230

    Article  Google Scholar 

  25. Shi Q, Yin S, Wang K, Teng L, Li H (2022) Multichannel convolutional neural network-based fuzzy active contour model for medical image segmentation. Evol Syst 13(4):535–549

    Article  Google Scholar 

  26. Cuevas E, Reyna-Orta A (2014) A cuckoo search algorithm for multimodal optimization. Sci World J, 2014

  27. Wang L, Qian X, Zhang Y, Shen J, Cao X (2019) Enhancing sketch-based image retrieval by cnn semantic re-ranking. IEEE Trans Cybern 50(7):3330–3342

    Article  Google Scholar 

  28. Shen J, Robertson N (2021) BBAS: Towards large scale effective ensemble adversarial attacks against deep neural network learning. Inf Sci 569:469–478

    Article  Google Scholar 

  29. Dwivedi N, Singh DK, Kushwaha DS (2023) A novel approach for suspicious activity detection with deep learning. Multimed Tools Appl, 1-24

Download references

Author information

Authors and Affiliations

  1. Department of Electronics and Communication Engineering, MLR Institute of Technology, Hyderabad, India

    S. V. S Prasad

  2. Department of Electronics and Communication Engineering, Raghu Engineering College (Autonomous), Visakhapatnam, Andhra Pradesh, India

    B. Chinna Rao

  3. Department of Electronics and Communication Engineering, Sri Vasavi engineering College, Pedatadepalli, Tadepalligudem, Andhra Pradesh, India

    M. Koteswara Rao

  4. Department of Electronics and communication Engineering, N.S.Raju Institute of Technology, Visakhapatnam, Andhra Pradesh, India

    K. Ravi Kumar

  5. Department of Computer Science and Engineering, School of Technology, GITAM Deemed to be University, Hyderabad, India

    Srisailapu D. Vara Prasad

  6. Department of Computer Science and Engineering and Dean IQAC, Aditya Institute of Technology and Management, Tekkali, Andhra Pradesh, India

    Chappa Ramesh

Authors
  1. S. V. S Prasad
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. B. Chinna Rao
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. M. Koteswara Rao
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. K. Ravi Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Srisailapu D. Vara Prasad
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Chappa Ramesh
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to S. V. S Prasad.

Ethics declarations

Conflict of interest

The authors declare that we have no conflict of interest.

Additional information

Publisher’s note

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

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

Prasad, S.V.S., Rao, B.C., Rao, M.K. et al. Medical image segmentation using an optimized three-tier quantum convolutional neural network trained with hybrid optimization approach. Multimed Tools Appl 83, 38083–38108 (2024). https://doi.org/10.1007/s11042-023-16980-9

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-023-16980-9

Keywords

Search

Navigation