Advertisement

Journal of Medical Systems

, 42:224 | Cite as

Medical Image Quality Assessment Using CSO Based Deep Neural Network

  • J. Jayageetha
  • C. Vasanthanayaki
Image & Signal Processing
Part of the following topical collections:
  1. Image & Signal Processing

Abstract

This manuscript proposed a hybrid method of Deep Neural Network (DNN) and Cuckoo Search Optimization (CSO) with No-Reference Image Quality Assessment (NR-IQA) for achieving high accuracy, low computational complexity, flexibility and etc. of a medical image. NR-IQA is proposed due to till now there is no perfect reference image for finding the quality of real time medical imaging. It is an effective method for assessing the real-world medical images. The proposed method takes the distorted image as an input and estimate the quality of the image without the assistance of reference image. The techniques CSO and DNN with NR-IQA produces the quality of the image with high quality score and low Mean Square Error (MSE). Also, the proposed method is used to improve the quality score thereby improving the quality of the image. So that the resultant image has good visual properties which is useful for the analysis of further medical proceedings. The simulation result shows that the proposed system improves the quality score by 8% when compared to the other existing systems. The SROCC value can be increased as 6%, 14%, 6 and 2% for the different existing methods such as NR-BIQA, SBVQP-ML, PTQL/PTVC and NR-SIQA (3D) respectively.

Keywords

Regression Deep neural network Cuckoo search optimization (CSO) No-reference image quality assessment (NR-IQA) 

References

  1. 1.
    Sheikh, H., Bovik, A., and Veciana, G., An information fidelity criterion for image quality assessment using natural scene statistics. IEEE Trans. Image Process. 14(12):2117–2128, 2005.CrossRefGoogle Scholar
  2. 2.
    Parvez Sazzad, Z., Kawayoke, Y., and Horita, Y., No reference image quality assessment for jpeg2000 based on spatial features. Signal Process. Image Commun. 23(4):257–268, 2008.CrossRefGoogle Scholar
  3. 3.
    Kavukcuoglu, K., Sermanet, P., and LeCun, Y., Learning convolutional feature hierachies for visual recognition. In: Advances in neural information processing systems (NIPS), 2010.Google Scholar
  4. 4.
    Kang, L., Li, Y., and Doermann, D., Convolutional neural networks for no-reference image quality assessment. In: IEEE conference on computer vision and pattern recognition, 2014.Google Scholar
  5. 5.
    Abdalmajeed, S., and Shuhong, J., An algorithm for no-reference image quality assessment based on log-derivative statistics of natural scenes. Electron. Lett. 50(8), 2014.Google Scholar
  6. 6.
    Cohen, E., Zhak, Y., and Itzhaky, Y., No-reference assessment of blur and noise impacts on image quality. SIViP 4(3):289–302, 2010.CrossRefGoogle Scholar
  7. 7.
    Zhang, C., He, K., and Wu, X., No-reference image quality assessment using Euclidean distance matrices. Adv. inform. Sci. Serv. Sci. 1:6, 2014.Google Scholar
  8. 8.
    R. Dosselmann and X. Yang, “No-Reference Image Quality Assessment using Level-of-Detail”, Technical Report CS 2011–2, 2011.Google Scholar
  9. 9.
    Y. Fu and S. Wang, “No-Reference Image Quality Assessment Based on HVS”, International Symposium on Computer, Consumer and Control (IS3C), 2016.Google Scholar
  10. 10.
    A. Puntura, N. Theera-Umpon and S Auephanwiriyakul, “Optimizing Support Vector Machine Parameters Using Cuckoo Search Algorithm via Cross Validation”,IEEE International Conference on Control System, Computing and Engineering (ICCSCE),2016.Google Scholar
  11. 11.
    Y. Li, L. Man Po, L.Feng and F. Yuan, No-reference image quality assessment based ondeep learning method. IEEE Int. Conf. Digit. Sign. Proc. (DSP), 2017.Google Scholar
  12. 12.
    Bosse, S., Maniry, D., Wiegand, T., and Samek, W., Deep neural networks for no-reference and full-reference image quality assessment. IEEE Trans. Image Process. 27(1):206–219, 2018.CrossRefGoogle Scholar
  13. 13.
    Hou, W., Gao, X., Tao, D., and Li, X., Blind image quality assessment via deep learning. IEEE Trans. Neural Netw. Learn. Syst. 26(6):1275–1286, 2015.CrossRefGoogle Scholar
  14. 14.
    L. Kang, P. Ye, Y. Li and D. Doermann, A Deep Learning Approach to Document Image Quality Assessment. IEEE Int. Conf. Image Proc. (ICIP), 2014.Google Scholar
  15. 15.
    Moorthy, A. K., and Bovik, A. C., Blind image quality assessment: From natural scene statistics to perceptual quality. IEEE Trans. Image Process. 20(12):3350–3364, 2011.CrossRefGoogle Scholar
  16. 16.
    R. Akhter, J. Baltes, Z. Parvez Sazzad and Y. Horita, No reference stereoscopic image quality assessment. Proc. Soc. Photo Opt. Instrum. Eng., 7524, 2010, Art. no. 75240T.Google Scholar
  17. 17.
    Shen, L., Fang, R., Yao, Y., and Wu, D., No-reference stereoscopic image quality assessment based on image distortion and stereo perceptual information. IEEE Trans. Emerg. Topics Comput. Intel. 99:1–14, 2018.Google Scholar
  18. 18.
    Chen, M., Cormack, L., and Bovik, A., No-reference quality assessment of natural stereopairs. IEEE Trans. Image Process. 22(9):3379–3391, 2013.CrossRefGoogle Scholar
  19. 19.
    S. Wang, F. Shao and G. Jiang, “Supporting binocular visual quality prediction using machine learning,” in Proc. 2014 IEEE Int. Conf. Multimedia Expo. Workshops. 1–6, 2014.Google Scholar
  20. 20.
    Shao, F., Lin, W., and Wang, S., blind image quality assessment for stereoscopic images using binocular guided quality lookup and visual codebook. IEEE trans. Broadcast. 61(2):154–165, 2015.CrossRefGoogle Scholar
  21. 21.
    Shen, L., Fang, R., Yao, Y., Geng, X., and Dapeng, W., No-reference stereoscopic image quality assessment based on image distortion and stereo perceptual information. IEEE Trans. Emerg. Topics Comput. Intel. 99:1–14, 2018.Google Scholar
  22. 22.
    Joshi, P., and Prakash, S., Retina inspired no-reference image quality assessment for blur and noise. Multimed. Tools Appl. 76(18):18871–18890, 2017.CrossRefGoogle Scholar
  23. 23.
    Ahilan, A., and Deepa, P., Radiation induced multiple bit upset mitigation and correction in memories using cost efficient CMC. Microelectron. Electron. Comp. Mater. 46(4):257–266, 2016.Google Scholar
  24. 24.
    Ahilan, A., and Deepa, P., “Design for Reliability: A novel counter matrix code for FPGA based quality applications”, 6th Asia symposium on quality electronic design electronic design (ASQED 2015), Kuala Lumpur. Malaysia 6:56–61, 2015.Google Scholar
  25. 25.
    Ahilan, A., and Deepa, P., Design for Built in FPGA reliability via fine grained 2D error correction codes. Microelectron. Reliab. 55(9–10):2108–2112, 2015.CrossRefGoogle Scholar
  26. 26.
    Ahilan, A., and Deepa, P., Improving lifetime of memory devices using evolutionary computing based error correction coding. Comput. Intel. Cyber Sec. Comput. Models:237–245, 2016.Google Scholar
  27. 27.
    Li, S. C., and Paramesran, R., Review of medical image quality assessment. Biomed. Sign. Proc. Cont. 27:145–154, 2016.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of ECESNS College of TechnologyCoimbatoreIndia
  2. 2.Government College of TechnologyCoimbatoreIndia

Personalised recommendations