Your browser does not support JavaScript!
http://iet.metastore.ingenta.com
1887

Efficient medical image enhancement based on CNN-FBB model

Efficient medical image enhancement based on CNN-FBB model

For access to this article, please select a purchase option:

Buy article PDF
$19.95
(plus tax if applicable)
Buy Knowledge Pack
10 articles for $120.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Name:*
Email:*
Your details
Name:*
Email:*
Department:*
Why are you recommending this title?
Select reason:
 
 
 
 
 
IET Image Processing — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

Medical image quality requirements have been increasingly stringent with the recent developments of medical technology. To meet clinical diagnosis needs, an effective medical image enhancement method based on convolutional neural networks (CNNs) and frequency band broadening (FBB) is proposed. Curvelet transform is used to deal with medical data by obtaining the curvelet coefficient in each scale and direction, and the generalised cross-validation is implemented to select the optimal threshold for performing denoising processing. Meanwhile, the cycle spinning scheme is used to wipe off the visible ringing effects along the edges of medical images. Then, FBB and a new CNN model based on the retinex model are used to improve the processed image resolution. Eventually, pixel-level fusion is made between two enhanced medical images from CNN and FBB. In the authors’ study, 50 groups of medical magnetic resonance imaging, X-ray, and computed tomography images in total have been studied. The experimental results indicate that the final enhanced image using the proposed method outperforms other methods. The resolution and the edge details of the processed image are significantly enhanced, providing a more effective and accurate basis for medical workers to diagnose diseases.

References

    1. 1)
      • 2. Zhan, K., Shi, J., Teng, J., et al: ‘Linking synaptic computation for image enhancement’, Neurocomputing, 2017, 238, (238), pp. 112.
    2. 2)
      • 8. Daniel, E., Anitha, J.: ‘Optimum wavelet based masking for the contrast enhancement of medical images using enhanced cuckoo search algorithm’, Comput. Biol. Med., 2016, 71, (71), pp. 149155.
    3. 3)
      • 21. Bhutada, G.G., Anand, R.S., Saxena, S.C.: ‘Edge preserved image enhancement using adaptive fusion of images denoised by wavelet and curvelet transform’, Digit. Signal Process., 2011, 21, (1), pp. 118130.
    4. 4)
      • 32. Tong, Y., Zhao, M., Wei, Z., et al: ‘Synthetic aperture radar image nonlinear enhancement algorithm based on NSCT transform’, Phys. Commun., 2014, 13, pp. 239243.
    5. 5)
      • 43. Rundo, L., Tangherloni, A., Nobile, M.S.: ‘MedGA: a novel evolutionary method for image enhancement in medical imaging systems’, Expert Syst. Appl., 2019, 119, pp. 387399.
    6. 6)
      • 28. Sun, X., Liu, L., Dong, J.: ‘Underwater image enhancement with encoding–decoding deep CNN networks’. 2017 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computed, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI), San Francisco, CA, USA, 2017, pp. 16.
    7. 7)
      • 37. Dong, L., Yang, Q., Wu, H., et al: ‘High quality multi-spectral and panchromatic image fusion technologies based on curvelet transform’, Neurocomputing, 2015, 159, pp. 268274.
    8. 8)
      • 22. Choi, Y., Kim, N., Hwang, S.: ‘Thermal image enhancement using convolution neural network’. IEEE/RSJ Int. Conf. Intelligent Robots & Systems, Daejeon, Korea, October 2016.
    9. 9)
      • 20. Anand, S., Kumari, R.S., Thivya, T., et al: ‘Sharpening enhancement of ultrasound images using contourlet transform’, Opt.-Int. J. Light Electron. Opt., 2013, 124, (21), pp. 47894792.
    10. 10)
      • 15. Arivazhagan, S., Deivalakshmi, S., Kannan, K., et al: ‘Multi-resolution system for artifact removal and edge enhancement in computerized tomography images’, Pattern Recognit. Lett., 2007, 28, (13), pp. 17691780.
    11. 11)
      • 46. Zhao, C., Wang, Z., Li, H., et al: ‘A new approach for medical image enhancement based on luminance-level modulation and gradient modulation’, Biomed. Signal Proc. Control, 2019, 48, pp. 189196.
    12. 12)
      • 41. Li, J., Qiu, T., Wen, C., et al: ‘Robust face recognition using the deep C2D 2D-CNN model based on decision-level fusion’, Sensors, 2018, 18, (7), p. 2080.
    13. 13)
      • 26. Li, C., Guo, J., Porikli, F., et al: ‘LightenNet: a convolutional neural network for weakly illuminated image enhancement’, Pattern Recognit. Lett., 2018, 5, pp. 1522.
    14. 14)
      • 12. Jiang, G., Lin, S.C., Wong, C.Y., et al: ‘Color image enhancement with brightness preservation using a histogram specification approach’, Opt.-Int. J. Light Electron. Opt., 2015, 12, (24), pp. 56565664.
    15. 15)
      • 39. Yang, S., Zhang, J., Cui, S., et al: ‘Curvelet support value filters (CSVFs) for image super resolution’, Neurocomputing, 2016, 211, pp. 5359.
    16. 16)
      • 45. Wang, X., Chen, L.: ‘Contrast enhancement using feature-preserving bi-histogram equalization’, Signal Image Video Process., 2018, 12, (4), pp. 685692.
    17. 17)
      • 27. Tao, L., Zhu, C., Song, J., et al: ‘Low-light image enhancement using CNN and bright channel prior’. 2017 IEEE Int. Conf. Image Processing (ICIP), Beijing, China, September 2017, pp. 32153219.
    18. 18)
      • 23. Liao, X., Zhang, X.: ‘Multi-scale mutual feature convolutional neural network for depth image denoise and enhancement’. 2017 IEEE Visual Communications and Image Processing (VCIP), St. Petersburg, USA, December 2017, pp. 14.
    19. 19)
      • 6. Hasan, H., Abdul-Kareem, S.: ‘Fingerprint image enhancement and recognition algorithms: a survey’, Neural Comput. Appl., 2013, 23, (6), pp. 16051610.
    20. 20)
      • 5. Matiolański, A., Maksimova, A., Dziech, A.: ‘CCTV object detection with fuzzy classification and image enhancement’, Multimedia Tools Appl., 2016, 75, (17), pp. 1051310528.
    21. 21)
      • 35. Hadjadji, B., Chibani, Y., Nemmour, H.: ‘An efficient open system for offline handwritten signature identification based on curvelet transform and one-class principal component analysis’, Neurocomputing, 2017, 11, pp. 6677.
    22. 22)
      • 31. Asmare, M.H., Asirvadam, V.S., Hani, A.F.: ‘Image enhancement based on contourlet transform’, Signal Image Video Process., 2015, 9, (7), pp. 16791690.
    23. 23)
      • 4. Rahman, S., Rahman, M.M., Abdullah-Al-Wadud, M., et al: ‘An adaptive gamma correction for image enhancement’, EURASIP J. Image Video Process., 2016, 2016, (1), p. 35.
    24. 24)
      • 11. Li, B., Xie, W.: ‘Adaptive fractional differential approach and its application to medical image enhancement’, Comput. Electr. Eng., 2015, 45, (45), pp. 324335.
    25. 25)
      • 1. Zhang, S., Wang, T., Dong, J., et al: ‘Underwater image enhancement via extended multi-scale retinex’, Neurocomputing, 2017, 7, (245), pp. 19.
    26. 26)
      • 34. Li, Y., Hu, J., Jia, Y.: ‘Automatic SAR image enhancement based on nonsubsampled contourlet transform and memetic algorithm’, Neurocomputing, 2014, 6, pp. 7078.
    27. 27)
      • 24. Tao, Y., Duan, J., Liang, X., et al: ‘An improved method of retinex for night color image enhancement’.
    28. 28)
      • 16. Yang, S.F., Cheng, C.H.: ‘Fast computation of Hessian-based enhancement filters for medical images’, Comput. Methods Programs Biomed., 2014, 116, (3), pp. 215225.
    29. 29)
      • 38. Acharya, U.R., Raghavendra, U., Fujita, H., et al: ‘Automated characterization of fatty liver disease and cirrhosis using curvelet transform and entropy features extracted from ultrasound images’, Comput. Biol. Med., 2016, 79, pp. 250258.
    30. 30)
      • 44. Agarwal, M., Mahajan, R.: ‘Medical image contrast enhancement using range limited weighted histogram equalization’, Procedia Comput. Sci., 2018, 125, pp. 149156.
    31. 31)
      • 14. Zeng, M., Li, Y., Meng, Q., et al: ‘Improving histogram-based image contrast enhancement using gray-level information histogram with application to X-ray images’, Opt.-Int. J. Light Electron. Opt., 2012, 123, (6), pp. 511520.
    32. 32)
      • 30. Kim, S.E., Jeon, J.J., Eom, I.K.: ‘Image contrast enhancement using entropy scaling in wavelet domain’, Signal Process., 2016, 10, p. 1.
    33. 33)
      • 40. Guo, X., Li, Y., Ling, H.: ‘LIME: low-light image enhancement via illumination map estimation’, IEEE Trans. Image Process., 2017, 26, pp. 982993.
    34. 34)
      • 33. Wu, C., Liu, Z., Jiang, H.: ‘Catenary image enhancement using wavelet-based contourlet transform with cycle translation’, Opt.-Int. J. Light Electron. Opt., 2014, (15), pp. 39223925.
    35. 35)
      • 25. Fu, X., Sun, Y., LiWang, M., et al: ‘A novel retinex based approach for image enhancement with illumination adjustment’. 2014 IEEE Int. Conf. Acoustics, Speech and Signal Processing (ICASSP), Florence, Italy, May 2014, pp. 11901194.
    36. 36)
      • 19. Cao, W., Che, R., Ye, D.: ‘An illumination-independent edge detection and fuzzy enhancement algorithm based on wavelet transform for non-uniform weak illumination images’, Pattern Recognit. Lett., 2008, 29, (3), pp. 192199.
    37. 37)
      • 36. Jiang, X., Ding, H., Zhang, H., et al: ‘Study on compressed sensing reconstruction algorithm of medical image based on curvelet transform of image block’, Neurocomputing, 2017, 220, pp. 191198.
    38. 38)
      • 7. Gandhamal, A., Talbar, S., Gajre, S., et al: ‘Local gray level s-curve transformation – a generalized contrast enhancement technique for medical images’, Comput. Biol. Med., 2017, 83, (83), pp. 120133.
    39. 39)
      • 9. Moreno, R., Smedby, Ö.: ‘Gradient-based enhancement of tubular structures in medical images’, Med. Image Anal., 2015, 26, (1), pp. 1929.
    40. 40)
      • 42. Jia, Y., Shelhamer, E., Donahue, J., et al: ‘Caffe: convolutional architecture for fast feature embedding’. Proc. 22nd ACM Int. Conf. Multimedia, Orlando, FL, USA, November 2014, pp. 675678.
    41. 41)
      • 18. Kurobe, Y., Kitagawa, K., Ito, T., et al: ‘Myocardial delayed enhancement with dual-source CT: advantages of targeted spatial frequency filtration and image averaging over half-scan reconstruction’, J. Cardiovasc. Comput. Tomogr., 2014, 8, (4), pp. 289298.
    42. 42)
      • 10. Wu, H.T., Huang, J., Shi, Y.Q.: ‘A reversible data hiding method with contrast enhancement for medical images’, J. Vis. Commun. Image Represent., 2015, 31, (31), pp. 146153.
    43. 43)
      • 29. Land, E.H., McCann, J.J.: ‘Lightness and retinex theory’, J. Opt. Soc. Am., 1971, 61, (1), p. 1.
    44. 44)
      • 3. Zhuang, P., Fu, X., Huang, Y., et al: ‘Image enhancement using divide-and-conquer strategy’, J. Vis. Commun. Image Represent., 2017, 45, (45), pp. 137146.
    45. 45)
      • 13. Isa, I.S., Sulaiman, S.N., Mustapha, M., et al: ‘Automatic contrast enhancement of brain MR images using average intensity replacement based on adaptive histogram equalization (AIR-AHE)’, Biocybern. Biomed. Eng., 2017, 37, (1), pp. 2434.
    46. 46)
      • 17. Decker, C.M., Zöllner, F.G., Konstandin, S., et al: ‘Comparing anisotropic diffusion filters for the enhancement of sodium magnetic resonance images’, Magn. Reson. Imaging, 2012, 30, (8), pp. 11921200.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-ipr.2018.6380
Loading

Related content

content/journals/10.1049/iet-ipr.2018.6380
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
This is a required field
Please enter a valid email address