Nothing Special   »   [go: up one dir, main page]
Skip to main content

Advertisement

Springer Nature Link
Log in

Efficient tumor volume measurement and segmentation approach for CT image based on twin support vector machines

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

Suspicious volumetric tumor (SVT) segmentation of a CT-image (CT\(_{i}\)) and analysing changes in the volume of tumor is a significantly challenging task for the identification of lung cancer. In this regard, we design a two-step suspicious volumetric tumor segmentation (SVTS) approach based on an adaptive multiple resolution contour (AMRC) models for effective SVT segmentation. First, the high-intensity-pixels edge centroid of SVT (HECS) method is designed to identify the SVT location in CT\(_{i}\), and these outcomes are subsequently conceding threshold values to fix the level set method (LSM). Second, HECS outcomes are recognised using particle swarm optimisation (PSO) which is harmonised twin support vector machines (TSVM) to achieve segmentation accuracy. An open-source tumor cancer imaging archive (TCIA) dataset, 529 abnormal tissues (ATs) of the lung from the lung image database consortium (LIDC), are conceded to assess the performance of the SVT segmentation approach. The average segmentation accuracy of NLTC, TCIA, and LIDC datasets are 73.19%, 76.21% and 75.89%, respectively, compared with standard benchmark approaches. Subsequently, our framework efficiently classified the normal and abnormal CT\(_{i}\) based on the SVT segmentation accuracy rate.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  1. Zilong H, Tang J, Wang Z, Zhang K, Zhang L, Sun Q (2018) Deep learning for image-based cancer detection and diagnosis–a survey. Pattern Recognit 83:134–149

    Article  Google Scholar 

  2. Ferlay J, Colombet M, Soerjomataram I, Parkin DM, Piñeros M, Znaor A, Bray F(2021) Cancer statistics for the year 2020: an overview. Int J Cancer

  3. Chung-Feng JK, Kuan HL, Weng W-H, Barman J, Huang C-C, Chiu C-W, Lee J-L, Hsu H-H (2021) Complete fully automatic segmentation and 3-dimensional measurement of mediastinal lymph nodes for a new response evaluation criteria for solid tumors. Biocybern Biomed Eng 41(2):617–635

    Article  Google Scholar 

  4. Ghaffari M, Sowmya A, Oliver R (2020) Automated brain tumor segmentation using multimodal brain scans: a survey based on models submitted to the brats 2012–2018 challenges. IEEE Rev Biomed Eng 13:156–168

    Article  Google Scholar 

  5. 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 

  6. Chen C, Zhou K, Zha M, Qu X, Guo X, Chen H, Wang Z, Xiao R (2021) An effective deep neural network for lung lesions segmentation from Covid-19 CT images. IEEE Trans Ind Inform

  7. Zhang M, Li H, Pan S, Lyu J, Ling S, Su S (2021) Convolutional neural networks-based lung nodule classification: a surrogate-assisted evolutionary algorithm for hyperparameter optimization. IEEE Trans Evolut Comput

  8. Jena SR, George ST, Ponraj DN (2021) Lung cancer detection and classification with DGMM-RBCNN technique. Neural Comput Appl. https://doi.org/10.1007/s00521-021-06182-5

    Article  Google Scholar 

  9. Vijh S, Gaurav P, Pandey HM (2020) Hybrid bio-inspired algorithm and convolutional neural network for automatic lung tumor detection. Neural Comput Appl 1–14

  10. Pramod Kumar S, Latte Mrityunjaya V, Siri Sangeeta K (2020) Volumetric lung nodule segmentation in thoracic CT scan using freehand sketch. IET Image Process 14(14):3456–3462

    Article  Google Scholar 

  11. Setio AA, Ciompi F, Litjens G, Gerke P, Jacobs C, Van Riel SJ, Wille MM, Naqibullah M, Sánchez CI, Van Ginneken B (2016) Pulmonary nodule detection in CT images: false positive reduction using multi-view convolutional networks. IEEE Trans Med Imaging 35(5):1160–1169

    Article  Google Scholar 

  12. Jordan C, Kocher Madison R, Jeffrey W, Madalyn S, Stringer Natalie FC, Joseph S, Pooyan S, Puneet S, Saikiran R, Joseph SU et al (2021) Automated detection of lung nodules and coronary artery calcium using artificial intelligence on low-dose CT scans for lung cancer screening: accuracy and prognostic value. BMC Med 19(1):1–14

    Article  Google Scholar 

  13. Shen W, Zhou M, Yang F, Yu D, Dong D, Yang C, Zang Y, Tian J (2017) Multi-crop convolutional neural networks for lung nodule malignancy suspiciousness classification. Pattern Recognit 61:663–673

    Article  Google Scholar 

  14. Kailasam MS, Thiagarajan MD (2021) Detection of lung tumor using dual tree complex wavelet transform and co-active adaptive neuro fuzzy inference system classification approach. Int J Imaging Syst Technol 31(4):2032–2046

    Article  Google Scholar 

  15. Woźniak M, Siłka J, Wieczorek M (2021) Deep neural network correlation learning mechanism for CT brain tumor detection. Neural Comput Appl. https://doi.org/10.1007/s00521-021-05841-x

    Article  Google Scholar 

  16. Chahal PK, Pandey S (2021) A hybrid weighted fuzzy approach for brain tumor segmentation using MR images. Neural Comput Appl. https://doi.org/10.1007/978-981-13-9939-8_24

    Article  Google Scholar 

  17. Rajan PG, Sundar C (2019) Brain tumor detection and segmentation by intensity adjustment. J Med Syst 43(8):1–13

    Article  Google Scholar 

  18. Zhou T, Canu S, Vera P, Ruan S (2021) Latent correlation representation learning for brain tumor segmentation with missing MRI modalities. IEEE Trans Image Process 30:4263–4274

    Article  Google Scholar 

  19. Aghamohammadi A, Ranjbarzadeh R, Naiemi F, Mogharrebi M, Dorosti S, Bendechache M (2021) TPCNN: Two-path convolutional neural network for tumor and liver segmentation in CT images using a novel encoding approach. Expert Syst Appl. https://doi.org/10.1016/j.eswa.2021.115406

    Article  Google Scholar 

  20. Alhassan AM, Zainon WM (2021) Brain tumor classification in magnetic resonance image using hard swish-based relu activation function-convolutional neural network. Neural Comput Appl. https://doi.org/10.1007/s00521-020-05671-3

    Article  Google Scholar 

  21. Jamal AT, Ishak AB, Abdel-Khalek S (2021) Tumor edge detection in mammography images using quantum and machine learning approaches. Neural Comput Appl. https://doi.org/10.1007/s00521-020-05518-x

    Article  Google Scholar 

  22. Jiang H, Ma H, Qian W, Gao M, Li Y (2017) An automatic detection system of lung nodule based on multigroup patch-based deep learning network. IEEE J Biomed Health Inform 22(4):1227–1237

    Article  Google Scholar 

  23. Sayan K, Kanta SJ, Kumar SP (2019) A new entropy-based approach for fuzzy c-means clustering and its application to brain MR image segmentation. Soft Comput 23(20):10407–10414

    Article  Google Scholar 

  24. ALzubi JA, Bharathikannan B, Tanwar S, Manikandan R, Khanna A, Thaventhiran C (2019) Boosted neural network ensemble classification for lung cancer disease diagnosis. Appl Soft Comput 80:579–591

    Article  Google Scholar 

  25. Lakshmanaprabu SK, Nandan MS, Shankar K, Arunkumar N, Gustavo R (2019) Optimal deep learning model for classification of lung cancer on CT images. Future Gener Comput Syst 92:374–382

    Article  Google Scholar 

  26. Mohamed SP, Ishak DM, Burhanuddin MA (2020) Improved watershed histogram thresholding with probabilistic neural networks for lung cancer diagnosis for CBMIR systems. Multimed Tools Appl 79(23):17115–17133

    Article  Google Scholar 

  27. Bansal G, Chamola V, Narang P, Kumar S, Raman S (2020) Deep3Dscan: Deep residual network and morphological descriptor-based framework for lung cancer classification and 3D segmentation. IET Image Process 14(7):1240–1247

    Article  Google Scholar 

  28. Yan K, Tian L, Xueqiao P, Elvis SG, Qiao L, Junyi Z (2020) Unsupervised multi-discriminator generative adversarial network for lung nodule malignancy classification. IEEE Access 8:77725–77734

    Article  Google Scholar 

  29. Xie Y, Zhang J, Xia Y (2019) Semi-supervised adversarial model for benign-malignant lung nodule classification on chest CT. Med Image Anal 57:237–248

    Article  Google Scholar 

  30. Revathi J, Anitha J, Rizwan P, Manikandan R, Hemanth DJ, Gandomi AH (2021) Machine learning-based left ventricular hypertrophy detection using multi-lead ECG signal. Neural Comput Appl 33(9):4445–4455

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported in part of Basic Science Research Programs of the Ministry of Education (NRF-2018R1A2B6005105) and in part by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2019R1A5A8080290).

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Tirumala Engineering College, Narsaraopet, AP, India

    K. Sathish & Y. V. Narayana

  2. Department of Information and Communication Engineering, Yeungnam University, Gyeongsan, 38544, Republic of Korea

    M. S. Mekala

  3. RLRC Lab for Autonomous Vehicle Parts and Materials Innovation, Yeungnam University, Gyeongsan, 38544, Republic of Korea

    M. S. Mekala

  4. Decentralized Science Lab (DSL), Department of Software Engineering & Game Development, College of Computing and Software Engineering, Kennesaw State University, 680 Arntson Drive, Marietta, 30060, USA

    Patan Rizwan

  5. Department of Computer Science and Engineering, Sree Vidyanikethan Engineering College, Tirupati, AP, India

    Suresh Kallam

Authors
  1. K. Sathish
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Y. V. Narayana
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. M. S. Mekala
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Patan Rizwan
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Suresh Kallam
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to M. S. Mekala or Patan Rizwan.

Ethics declarations

Conflict of interest

There is no conflict of interest between the authors to publish this article.

Additional information

Publisher's Note

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

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sathish, K., Narayana, Y.V., Mekala, M.S. et al. Efficient tumor volume measurement and segmentation approach for CT image based on twin support vector machines. Neural Comput & Applic 34, 7199–7207 (2022). https://doi.org/10.1007/s00521-021-06769-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-021-06769-y

Keywords

Search

Navigation