Skip to main content
Log in

Pseudocolor enhancement of mammogram texture abnormalities

Machine Vision and Applications Aims and scope Submit manuscript

Cite this article


We present a novel method for enhancing texture irregularities, both lesions and microcalcifications, in digital X-ray mammograms. It can be implemented in computer-aided diagnostic systems to help improve radiologists’ diagnosis precision. The method provides three different outputs aimed at enhancing three different sizes of mammogram abnormalities. Our approach uses a two-dimensional adaptive causal autoregressive texture model to represent local texture characteristics. Based on these, we enhance suspicious breast tissue abnormalities, such as microcalcifications and masses, to make signs of developing cancer better visually discernible. We extract over 200 local textural features from different frequency bands, which are then combined into a single multichannel image using the Karhunen–Loeve transform. We propose an extension to existing contrast measures for the evaluation of contrast around regions of interest. Our method was extensively tested on the INbreast database and compared both visually and numerically with three state-of-the-art enhancement methods, with favorable results.

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

Access this article

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

Similar content being viewed by others


  1. Bhateja, V., Misra, M., Urooj, S.: Non-linear polynomial filters for edge enhancement of mammogram lesions. Comput. Methods Progr. Biomed. 129, 125–134 (2016)

    Article  Google Scholar 

  2. Chang, C.M., Laine, A.: Coherence of multiscale features for enhancement of digital mammograms. IEEE Trans. Inf. Technol. Biomed. 3(1), 32–46 (1999)

    Article  Google Scholar 

  3. Dippel, S., Stahl, M., Wiemker, R., Blaffert, T.: Multiscale contrast enhancement for radiographies: Laplacian pyramid versus fast wavelet transform. IEEE Trans. Med. Imaging 21(4), 343–353 (2002)

    Article  Google Scholar 

  4. Erdem, C.E., Sankur, B., Tekalp, A.M.: Performance measures for video object segmentation and tracking. IEEE Trans. Image Process. 13(7), 937–951 (2004)

    Article  Google Scholar 

  5. Grim, J., Somol, P., Haindl, M., Daneš, J.: Computer-aided evaluation of screening mammograms based on local texture models. IEEE Trans. Image Process. 18(4), 765–773 (2009).

    Article  MathSciNet  MATH  Google Scholar 

  6. Haindl, M.: Texture synthesis. CWI Q. 4(4), 305–331 (1991)

    MATH  Google Scholar 

  7. Haindl, M.: Visual data recognition and modeling based on local markovian models. In: Florack, L., Duits, R., Jongbloed, G., Lieshout, M.C., Davies, L. (eds.) Mathematical Methods for Signal and Image Analysis and Representation, Computational Imaging and Vision, vol. 41, pp. 241–259. Springer, London (2012).

    Google Scholar 

  8. Haindl, M., Mikeš, S., Scarpa, G.: Unsupervised detection of mammogram regions of interest. In: Apolloni, B., Howlett, R.J., Jain, L. (eds.) Knowledge-Based Intelligent Information and Engineering Systems, LNAI, vol. 4694, pp. 33–40. Springer, Berlin (2007).

    Chapter  Google Scholar 

  9. Haindl, M., Remeš, V.: Efficient textural model-based mammogram enhancement. In: P.P. Rodrigues, M. Pechenizkiy, J. ao Gama, R. Cruz-Correia, J. Liu, A. Traina, P. Lucas, P. Soda (eds.) 2013 IEEE 26th International Symposium on Computer-Based Medical Systems (CBMS), pp. 522–523. IEEE, Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA (2013).

  10. Haindl, M., Šimberová, S.: A Multispectral Image Line Reconstruction Method. Theory and Applications of Image Analysis, pp. 306–315. World Scientific Publishing Co., Singapore (1992)

    Book  Google Scholar 

  11. Mencattini, A., Salmeri, M., Lojacono, R., Frigerio, M., Caselli, F.: Mammographic images enhancement and denoising for breast cancer detection using dyadic wavelet processing. IEEE Trans. Instrum. Meas. 57(7), 1422–1430 (2008)

    Article  Google Scholar 

  12. Moreira, I.C., Amaral, I., Domingues, I., Cardoso, A., Cardoso, M.J., Cardoso, J.S.: Inbreast: toward a full-field digital mammographic database. Acad. Radiol. 19(2), 236–248 (2012)

    Article  Google Scholar 

  13. Panetta, K., Zhou, Y., Agaian, S., Jia, H.: Nonlinear unsharp masking for mammogram enhancement. IEEE Trans. Inf. Technol. Biomed. 15(6), 918–928 (2011)

    Article  Google Scholar 

  14. Qi, H., Diakides, N.A.: Thermal infrared imaging in early breast cancer detection—a survey of recent research. In: Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, vol. 2, pp. 1109–1112. IEEE (2003)

  15. Remeš, V., Haindl, M.: Region of interest contrast measures. Kybernetika 54(5), 978–990 (2018).

  16. Sakellaropoulos, P., Costaridou, L., Panayiotakis, G.: A wavelet-based spatially adaptive method for mammographic contrast enhancement. Phys. Med. Biol. 48(6), 787 (2003)

    Article  Google Scholar 

  17. Salvado, J., Roque, B.: Detection of calcifications in digital mammograms using wavelet analysis and contrast enhancement. In: 2005 IEEE International Workshop on Intelligent Signal Processing, pp. 200–205. IEEE (2005)

  18. Simone, G., Pedersen, M., Hardeberg, J.Y.: Measuring perceptual contrast in digital images. J. Visual Commun. Image Represent. 23(3), 491–506 (2012)

    Article  Google Scholar 

  19. Tang, J., Liu, X., Sun, Q.: A direct image contrast enhancement algorithm in the wavelet domain for screening mammograms. IEEE J. Sel. Top. Signal Process. 3(1), 74–80 (2009).

    Article  Google Scholar 

  20. Taylor, P., Champness, J., Given-Wilson, R., Johnston, K., Potts, H.: Impact of computer-aided detection prompts on the sensitivity and specificity of screening mammography. Health Technol. Assess. 9(6), 1–58 (2005)

    Article  Google Scholar 

  21. Thangavel, K., Karnan, M., Sivakumar, R., Mohideen, A.: Cad system for preprocessing and enhancement of digital mammograms. Graphics, Vision and Image Processing pp. 55–60 (2007).

  22. Tweed, T., Miguet, S.: Automatic detection of regions of interest in mammographies based on a combined analysis of texture and histogram. In: 2002 Proceedings of 16th International Conference on Pattern Recognition, vol. 2, pp. 448–452. IEEE Computer Society, Los Alamitos, CA, USA (2002).

  23. Wang, H., Li, J.B., Wu, L., Gao, H.: Mammography visual enhancement in cad-based breast cancer diagnosis. Clin. Imaging 37(2), 273–282 (2013)

    Article  Google Scholar 

  24. Yan, Z., Zhang, Y., Liu, B., Zheng, J., Lu, L., Xie, Y., Liang, Z., Li, J.: Extracting hidden visual information from mammography images using conjugate image enhancement software. In: 2005 IEEE International Conference on, Information Acquisition, pp. 4775–4778. IEEE Engineering in Medicine and Biology Society, IEEE (2005)

Download references


This research was supported by the Czech Science Foundation Project GAČR 19-12340S.

Author information

Authors and Affiliations


Corresponding author

Correspondence to Michal Haindl.

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

Haindl, M., Remeš, V. Pseudocolor enhancement of mammogram texture abnormalities. Machine Vision and Applications 30, 785–794 (2019).

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI:


Mathematics Subject Classification