Kybernetika 54 no. 5, 978-990, 2018

Region of interest contrast measures

Václav Remeš and Michal HaindlDOI: 10.14736/kyb-2018-5-0978


A survey of local image contrast measures is presented and a new contrast measure for measuring the local contrast of regions of interest is proposed. The measures validation is based on the gradual objective contrast decreasing on medical test images in both grayscale and color. The performance of the eleven most frequented contrast measures is mutually compared and their robustness to different types of image degradation is analyzed. Since the contrast measures can be both global, regional and local pixelwise, a simple way of adapting the contrast measures for regions of interest is proposed.


contrast measures, image enhancement, enhancement quality measures, medical image enhancement


68U10, 94A08


  1. S. S. Agaian, K. Panetta and A. M. Grigoryan: Transform-based image enhancement algorithms with performance measure. IEEE Trans. Image Process. 10 (2001), 3, 367-382.   DOI:10.1109/83.908502
  2. S. S. Agaian, B. Silver and K. A. Panetta: Transform coefficient histogram-based image enhancement algorithms using contrast entropy. IEEE Trans. Image Process. 16 (2007), 3, 741-758.   DOI:10.1109/tip.2006.888338
  3. V. Bhateja, M. Misra and S. Urooj: Non-linear polynomial filters for edge enhancement of mammogram lesions. Comp. Meth. Programs Biomedicine 129 (2016), 125-134.   DOI:10.1016/j.cmpb.2016.01.007
  4. D. A. Burkhardt, J. Gottesman, D. Kersten and G. E. Legge: Symmetry and constancy in the perception of negative and positive luminance contrast. JOSA A 1 (1984), 3, 309-316.   DOI:10.1364/josaa.1.000309
  5. C.-M. Chang and A. Laine: Coherence of multiscale features for enhancement of digital mammograms. IEEE Trans. Inform. Technol. Biomedicine 3 (1999), 1, 32-46.   DOI:10.1109/4233.748974
  6. S. Dippel, M. Stahl, R. Wiemker and T. Blaffert: Multiscale contrast enhancement for radiographies: Laplacian pyramid versus fast wavelet transform. IEEE Trans. Medical Imaging 21 (2002), 4, 343-353.   DOI:10.1109/tmi.2002.1000258
  7. C. E. Erdem, B. Sankur and A. M. Tekalp: Performance measures for video object segmentation and tracking. IEEE Trans. Image Process. 13 (2004), 7, 937-951.   DOI:10.1109/tip.2004.828427
  8. J. Grim, P. Somol, M. Haindl and J. Daneš: Computer-aided evaluation of screening mammograms based on local texture models. IEEE Trans. Image Process. 18 (2009), 4, 765-773.   DOI:10.1109/tip.2008.2011168
  9. A. Haun and E. Peli: Perceived contrast in complex images. J. Vision 13 (2013), 3, 2013.   DOI:10.1167/13.13.3
  10. P. E. King-Smith and J. Kulikowski: Pattern and flicker detection analysed by subthreshold summation. J. Physiology 249 (1975), 3, 519.   DOI:10.1113/jphysiol.1975.sp011028
  11. M. D. Levine and A. M. Nazif: Dynamic measurement of computer generated image segmentations. IEEE Trans. Pattern Analysis Machine Intell. 7 (1985), 155-164.   DOI:10.1109/tpami.1985.4767640
  12. A. Mencattini, M. Salmeri, R. Lojacono, M. Frigerio and F. Caselli: Mammographic images enhancement and denoising for breast cancer detection using dyadic wavelet processing. IEEE Trans Instrument. Measurement 57 (2008), 7, 1422-1430.   DOI:10.1109/tim.2007.915470
  13. A. A. Michelson: Studies in Optics. University of Chicago Press, Chicago 1927.   CrossRef
  14. I. C. Moreira, I. Amaral, I. Domingues, A. Cardoso, M. J. Cardoso and J. S. Cardoso: Inbreast: toward a full-field digital mammographic database. Academic Radiology 19 (2012), 2, 236-248.   DOI:10.1016/j.acra.2011.09.014
  15. K. Panetta, Y. Zhou, S. Agaian and H. Jia: Nonlinear unsharp masking for mammogram enhancement. IEEE Trans. Inform. Technol. Biomedicine 15 (2011), 6, 918-928.   DOI:10.1109/titb.2011.2164259
  16. E. Peli: Contrast in complex images. JOSA A 7 (1990), 10, 2032-2040.   DOI:10.1364/josaa.7.002032
  17. H. Qi and N. A. Diakides: Thermal infrared imaging in early breast cancer detection - a survey of recent research. In: Proc. 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Vol. 2, IEEE 2003, pp. 1109-1112.   DOI:10.1109/iembs.2003.1279442
  18. P. Sakellaropoulos, L. Costaridou and G. Panayiotakis: A wavelet-based spatially adaptive method for mammographic contrast enhancement. Physics Medicine Biology 48 (2003), 6, 787.   DOI:10.1088/0031-9155/48/6/307
  19. J. Salvado and B. Roque: Detection of calcifications in digital mammograms using wavelet analysis and contrast enhancement. In: IEEE International Workshop on Intelligent Signal Processing 2005, IEEE 2005, pp. 200-205.   DOI:10.1109/wisp.2005.1531658
  20. G. Simone, M. Pedersen and J. Y. Hardeberg: Measuring perceptual contrast in digital images. J. Visual Commun. Image Representation 23 (2012), 3, 491-506.   DOI:10.1016/j.jvcir.2012.01.008
  21. Y. Tadmor and D. Tolhurst: Calculating the contrasts that retinal ganglion cells and \{LGN\} neurones encounter in natural scenes. Vision Research 40 (2000), 22, 3145-3157.   DOI:10.1016/s0042-6989(00)00166-8
  22. J. Tang, X. Liu and Q. Sun: A direct image contrast enhancement algorithm in the wavelet domain for screening mammograms. IEEE J. Selected Topics Signal Process. 3 (2009), 1, 74-80.   DOI:10.1109/jstsp.2008.2011108
  23. P. Taylor, J. Champness, R. Given-Wilson, K. Johnston and H. Potts: Impact of computer-aided detection prompts on the sensitivity and specificity of screening mammography. Health Technol. Assessment 9 (2005), 6.   DOI:10.3310/hta9060
  24. K. Thangavel, M. Karnan, R. Sivakumar and A. Mohideen: Cad system for preprocessing and enhancement of digital mammograms. Graphics, Vision Image Process. xx (2007), 55-60.   CrossRef
  25. T. Tweed and S. Miguet: Automatic detection of regions of interest in mammographies based on a combined analysis of texture and histogram. In: Proc. 16th International Conference on Pattern Recognition 2002, Vol. 2, Los Alamitos 2002. IEEE Computer Soc., pp. 448-452.   DOI:10.1109/icpr.2002.1048335
  26. H. Wang, J.-B. Li, L. Wu and H. Gao: Mammography visual enhancement in cad-based breast cancer diagnosis. Clinical Imaging 37 (2013), 273-282.   DOI:10.1016/j.clinimag.2012.04.018
  27. E. H. Weber: The Sense of Touch. Academic Press, 1978.   CrossRef
  28. P. Whittle: Increments and decrements: Luminance discrimination. Vision Res. 26 (1986), 10, 1677-1691.   DOI:10.1016/0042-6989(86)90055-6
  29. Z. Yan, Y. Zhang, B. Liu, J. Zheng, L. Lu, Y. Xie, Z. Liang and J. Li: Extracting hidden visual information from mammography images using conjugate image enhancement software. In: IEEE International Conference on Information Acquisition, IEEE Engineering in Medicine and Biology Society, 2005, pp. 4775-4778.   DOI:10.1109/icia.2005.1635092