Skip to main content

Advertisement

SpringerLink
Log in

Performance analysis of pre-cancerous mammographic image enhancement feature using non-subsampled shearlet transform

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

A Correction to this article was published on 14 July 2021

This article has been updated

Abstract

Breast cancer is the second highest death causing disease in female. From the data it’s clear that one of the eight women is affected by breast cancer. The most effective way to detect the cancer is by using mammography technique and also it detects masses and abnormal conditions. The cluster shaped white spots on mammograms are used the cancer in the early stages. The importance of breast cancer detection to reduce different noises which are commonly found in mammogram images and provides better image quality. Here the multi-scale and the multi-directional analysis would produce optimal approximation and detail coefficients of Non-subsampled shearlet transform (NSST). Hence in this paper, the noisy image is amended into piecewise smooth function in different subbands of frequency. NSST coefficients are amended into information and noise related coefficients are noise is removed by using the adaptive threshold. To evaluate the performance of this proposed algorithm, Mean Square Error (MSE) and Peak Signal-to-Noise Ratio (PSNR) are used. Testing these experimental results show that the proposed algorithm can preserve the edges and textures very well while weakening the noise can obtain better suppressed noise and enhances the objective of evaluations than other noise removal methods.

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

Change history

References

  1. AbuBaker AA (2015) Automatic microcalcification detection usingwavelettransform. Int J Comput Theory Eng 7(1):40–45

    Article  Google Scholar 

  2. Balan EV, Priyan MK, Gokulnath C, Devi GU (2015) Fuzzy based intrusion detection systems in MANET. Procedia Comput Sci 50:109–114

    Article  Google Scholar 

  3. Balan EV, Priyan MK, Devi GU (2015) Hybrid architecture with misuse and anomaly detection techniques for wireless networks. In: Communications and Signal Processing (ICCSP), 2015 International Conference on. IEEE, pp 0185–0189

  4. Devi GU, Balan EV, Priyan MK, Gokulnath C (2015) Mutual authentication scheme for IoT application. Indian J Sci Technol 8(26)

  5. Devi GU, Priyan MK, Balan EV, Nath CG, Chandrasekhar M (2015) Detection of DDoS attack using optimized hop count filtering technique. Indian J Sci Technol 8(26)

  6. Gokulnath C, Priyan MK, Balan EV, Prabha KR, Jeyanthi R (2015) Preservation of privacy in data mining by using PCA based perturbation technique. In: Smart technologies and management for computing, communication, controls, energy and materials (ICSTM), 2015 International Conference on. IEEE, pp 202–206

  7. Kumar PM, Gandhi UD (2017) A novel three-tier Internet of Things architecture with machine learning algorithm for early detection of heart diseases. Comput Electr Eng

  8. Kumar PM, Gandhi U, Varatharajan R, Manogaran G, Jidhesh R, Vadivel T (2017) Intelligent face recognition and navigation system using neural learning for smart security in internet of things. Clust Comput:1–12

  9. Lopez D, Manogaran G, Jagan J (2017) Modelling the H1N1 influenza using mathematical and neural network approaches. Biomed Res 28(8):1–5

    Google Scholar 

  10. Manogaran CTG, Priyan M (2017) Centralized fog computing security platform for IoT and cloud in healthcare system. Exploring the convergence of big data and the internet of things, pp 141

  11. Manogaran G, Varatharajan R, Lopez D, Kumar PM, Sundarasekar R, Thota C (2017) A new architecture of internet of things and big data ecosystem for secured smart healthcare monitoring and alerting system. Futur Gener Comput Syst

  12. Manogaran G, Varatharajan R, Priyan MK (2018) Hybrid recommendation system for heart disease diagnosis based on multiple kernel learning with adaptive neuro-fuzzy inference system. Multimedia Tools and Applications 77(4):4379–4399

    Article  Google Scholar 

  13. Priyan MK, Devi GU (2017) Energy efficient node selection algorithm based on node performance index and random waypoint mobility model in internet of vehicles. Clust Comput:1–15

  14. Priyan MK, Nath CG, Balan EV, Prabha KR, Jeyanthi R (2015) Desktop phishing attack detection and elimination using TSO program. In Smart Technologies and Management for Computing, Communication, Controls, Energy and Materials (ICSTM), 2015 International Conference on. IEEE, pp 198–201

  15. Rawal BS, Vijayakumar V, Manogaran G, Varatharajan R, Chilamkurti N Secure disintegration protocol for privacy preserving cloud storage. Wirel Pers Commun:1–17

  16. Varatharajan R, Manogaran G, Priyan MK, Sundarasekar R (2017) Wearable sensor devices for early detection of Alzheimer disease using dynamic time warping algorithm. Clust Comput:1–10

  17. Varatharajan R, Manogaran G, Priyan MK, Balaş VE, Barna C (2017) Visual analysis of geospatial habitat suitability model based on inverse distance weighting with paired comparison analysis. Multimedia Tools and Applications:1–21

  18. Varatharajan R, Manogaran G, Priyan MK (2017) A big data classification approach using LDA with an enhanced SVM method for ECG signals in cloud computing. Multimedia Tools and Applications:1–21

Download references

Acknowledgements

I would like to extend my heartfelt thanks to Dr.S.Saravanan, M.B.B.S.,DMRD.,(DNB). His interest, kindness, time to time suggestion, breast cancer detection ideas and result discussions led to the completion of this research paper.

Author information

Authors and Affiliations

  1. Department of Electronics and Communication Engineering, R.M.K. Engineering College, Chennai, India

    T. V. Padmavathy

  2. Department of Electronics and Communication Engineering, R.M.D. Engineering College, Chennai, India

    M. N. Vimalkumar

  3. Vellore Institute of Technology, Vellore, India

    Sivakumar Nagarajan, Gokul Chandra Babu & P. Parthasarathy

Authors
  1. T. V. Padmavathy
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. M. N. Vimalkumar
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sivakumar Nagarajan
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Gokul Chandra Babu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. P. Parthasarathy
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to T. V. Padmavathy.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Padmavathy, T.V., Vimalkumar, M.N., Nagarajan, S. et al. Performance analysis of pre-cancerous mammographic image enhancement feature using non-subsampled shearlet transform. Multimed Tools Appl 80, 26997–27012 (2021). https://doi.org/10.1007/s11042-018-5951-3

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-018-5951-3

Keywords

Search

Navigation