Key Points
Image processing is a crucial element of modern digital mammography. Optimizing mammogram presentation may lead to more efficient reading and improved diagnostic performance. Despite that the effects of image processing are often much larger than those of acquisition parameter settings, little is known about how image processing can be optimized. Experts agree that comparison of features in various mammographic views is very important. This issue must be addressed by processing. Variation of image presentation across views and subsequent mammograms should be minimized. The dynamic range of electronic displays is limited. Therefore, processing techniques should be designed to limit the dynamic range of mammograms. This can effectively be done by applying peripheral enhancement in the uncompressed tissue region near the projected skin—air interface. Adaptive contrast enhancement can be applied to enhance micro-calcifications and dense tissue in the interior of the mammogram. Mammogram processing should be aimed at displaying all relevant information in good contrast simultaneously, as human interaction to manipulate contrast during reading is too time-consuming to be applied on a regular basis.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Baydush AH, Floyd CE Jr (2000) Improved image quality in digital mammography with image processing. Med Phys 27(7):1503–1508
Burnside ES, Sickles EA, Sohlich RE et al (2002) Differential value of comparison with previous examinations in diagnostic versus screening mammography. AJR Am J Roentgenol 179(5):1173–1177
Byng JW, Critten J P, Yaffe MJ (1997) Thickness-equalization processing for mammographic images. Radiol 203:564–568
Cole EB, Pisano ED, Zeng D et al (2005) The effects of gray scale image processing on digital mammography interpretation performance. Acad Radiol 12(5):585–595
Heinlein P, Drexl J, Schneider W (2003) Integrated wavelets for enhancement of microcalcifications in digital mammography. IEEE Trans Med Imaging 22(3):402–413
Hemminger BM, Zong S, Muller KE et al (2001) Improving the detection of simulated masses in mammograms through two different image-processing techniques. Acad Radiol 8(9):845–855
Highnam R, Brady M (1999) Mammographic image analysis, 1st edn. Kluwer Academic Publishers, Dordrecht, the Netherlands
Kallergi M, Clarke LP, Qian W et al (1996) Interpretation of calcifications in screen/film, digitized, and wavelet-enhanced monitor-displayed mammograms: a receiver operating characteristic study. Acad Radiol 3(4):285–293
Kallergi M, Heine JJ, Berman CG et al (2004) Improved interpretation of digitized mammography with wavelet processing: a localization response operating characteristic study. AJR Am J Roentgenol 182(3):697–703
Laine AF, Schuler S, Fan J et al (1994) Mammographic feature enhancement by multiscale analysis. IEEE Trans Med Imaging 13(4):725–740
Pisano ED, Chandramouli J, Hemminger BM et al (1997) Does intensity windowing improve the detection of simulated calcifications in dense mammograms. J Digit Imaging 10 (2):79–84
Pisano ED, Cole EB, Hemminger BM et al (2000a) Image processing algorithms for digital mammography: a pictorial essay. Radiographics 20(5):1479–1491
Pisano ED, Cole EB, Major S et al (2000b) Radiologists' preferences for digital mammographic display. The international digital mammography development group. Radiology 216(3):820–830
Pizer SM, Johnston RE, Rogers DC et al (1987) Effective presentation of medical images on an electronic display station. Radiographics 7(6):1267–1274
Roelofs AA, Karssemeijer N, Wedekind N et al (2007) Importance of comparison of current and prior mammo-grams in breast cancer screening. Radiology 242(1):70–77
Snoeren PR, Karssemeijer N (2004) Thickness correction of mammographic images by means of a global parameter model of the compressed breast. IEEE Trans Med Imaging 23(7):799–806
Snoeren PR, Karssemeijer N (2005) Thickness correction of mammographic images by anisotropic filtering and interpolation of dense tissue. In: Fitzpatrick J, Reinhardt J (eds) SPIE medical imaging: image processing, vol. 5747, pp 1521–1527
Snoeren PR, Karssemeijer N (2007) Gray-scale and geometric registration of full-field digital and film-screen mammo-grams. Med Image Anal 11(2):146–156
Thurfjell MG, Vitak B, Azavedo E et al (2000) Effect on sensitivity and specificity of mammography screening with or without comparison of old mammograms. Acta Radiol 41(1):52–56
van Engeland S, Snoeren P, Karssemeijer N et al (2003) Optimized perception of lesion growth in mammograms using digital display. In: Chakraborty D, Krupinski E (eds) SPIE medical imaging: image perception. Observer Performance, and Technology Assessment, vol. 5034, pp 25–31
van Engeland S, Snoeren PR, Huisman H et al (2006) Volumetric breast density estimation from full-field digital mammo-grams. IEEE Trans Med Imaging 25(3):273–282
Veldkamp WJ, Thijssen MA, Karssemeijer N (2003) The value of scatter removal by a grid in full-field digital mammography. Med Phys 30(7):1712–1718
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Karssemeijer, N., Snoeren, P.R. (2010). Image Processing. In: Bick, U., Diekmann, F. (eds) Digital Mammography. Medical Radiology. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78450-0_5
Download citation
DOI: https://doi.org/10.1007/978-3-540-78450-0_5
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-78449-4
Online ISBN: 978-3-540-78450-0
eBook Packages: MedicineMedicine (R0)