Abstract
Mammography is a commonly used technique for early detection of breast cancer. In mammograms, microcalcifications show low contrast margin with the background parenchymal tissue (specifically when the background tissue type is fibroglandular) as a result, subjective analysis of these calcifications with respect to their size, shape and morphology presents a daunting challenge even for experienced radiologists. Thus the present work investigates the potential of two morphological techniques i.e., top-hat morphological processing and h-dome morphological processing for enhancement of microcalcifications embedded in variety of background tissue types including fatty, glandular and fibroglandular tissues while restoring their shape and size. The enhancement results are also compared with standard contrast limited adaptive histogram equalization method. For subjective analysis, 25 synthetic images with simulated microcalcifications of various shapes and sizes are used. Objective analysis is carried out on 50 mammographic images taken from benchmark dataset (McGill University mammographic database) by computing quantitative indices like contrast improvement ratio and detail variance/background variance ratios. After rigorous experimentation on both synthetic and benchmark data set it was observed that h-dome morphological processing (with h = 60) is ideally suited for enhancement of microcalcifications while restoring their shape and size.
Similar content being viewed by others
References
S. Feig, Decreased breast cancer mortality through mammographic screening: results of clinical trials. Radiology 167(3), 659–665 (1988)
S. Shaprio, W. Venet, P. Strax, L. Vanet, R. Roeser, Selection follow up, and analysis of the health insurance plan study: a randomized trial with breast cancer screening. J. Natl. Cancer Inst. Monogr. 67, 65 (1985)
L. Tabar, G. Faberberg, S. W. Duffy, N. E. Day, A. Gad, O. Grontoft, Update of the Swedish two-county program of mammographic screening for breast cancer. Radiol. Clin. North Am. 30(1), 187–210 (1992)
R.E. Bird, T.W. Wallace, B.C. Yankaskas, Analysis of cancers missed at screening mammography. Radiology 184(3), 613–617 (1992)
H.D. Chang, C. Xiaopeng, C. Xiaowei, H. Liming, L. Xueling, Computer-aided detection and classification of microcalcifications in mammograms: survey. J. Pattern Recognit. Soc. 2967 (2003)
A. Papadopoulos, D.I. Fotiadis, L. Costaridou, Improvement of microcalcification cluster detection in mammography utilizing image enhancement techniques. Comput. Biol. Med. 38, 1045 (2008)
A. Tomasz, K. Marcin, J.P. Tadeusz, O.D.S. Erik, A.Y. David, Detection of clustered microcalcifications in small field digital mammography. Comput. Methods Programs Biomed. 81, 56 (2006)
R. Branimir, M. Zorica, S. Tomislav, R. Irini, Computer aided system for segmentation and visualization of microcalcifications in digital mammograms’. Folia Histochem. Cytobiol. 47(3), 525 (2009)
Y. Kimori, Mathematical morphology-based approach to the enhancement of morphological features in medical images. J. Clin. Bioinform. 1, 33 (2011)
S. Singh, V. Kumar, H.K. Verma, D. Singh, SVM based system for classification of microcalcifications in digital mammograms. In: 28th annual international conference of the IEEE engineering in medicine and biology society EMBS ‘06, p. 4747 (2006)
B.S. Khehra, A.P.S. Pharwaha, Integration of Fuzzy and wavelet approaches towards mammogram contrast enhancement. J. Inst. Eng. (India) 1 (2012)
S.M. Pizer, E.O.P. Amburn, J.D. Austin, Adaptive histogram equalization and its variations. Comput. Vis. Graph. Image Process 39, 355 (1987)
R.C. Gonzalez, R.E. Woods, Digital image processing, 2nd edn. (Pearson Education Publishing, Upper Saddle River, 2004)
J.C. Fu, S.K. Lee, S.T.C. Wong, J.Y. Yeh, A.H. Wang, H.K. Wu, Image segmentation feature selection and pattern classification for mammographic microcalcifications. Comput. Med. Imaging Graph. 29, 419 (2005)
W. Michael, F. Matteo, L. Jennifer L, Contrast enhancement of microcalcifications in mammograms using morphological enhancement and non-flat structuring elements. In: Proceedings of the 17th IEEE symposium on computer-based medical systems (CBMS’04), p.134 (2004)
L. Vincent, Morphological gray scale reconstruction in image analysis: applications and efficient algorithms. IEEE Trans. Image Process. 2(2), 176 (1993)
H. Stelios, B. Taxiarchis, R. Maria, Automatic detection of clustered microcalcifications in digital mammograms using mathematical morphology and neural networks. Signal Process. 87, 1559 (2007)
A.F. Laine, S. Schuler, J. Fan, W.W. Huda, Mammographic feature enhancement by multiscale analysis. IEEE Trans. Med. Imaging 13(4), 725 (1994)
A. Vanzo, G. Ramponi, G.L. Siearanza, An image enhancement technique using polynomial filters. In: Proceedings of 1st IEEE-international conference on image processing, Austin, USA, p. 477 (1994)
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Jagannath, H.S., Virmani, J. & Kumar, V. Morphological Enhancement of Microcalcifications in Digital Mammograms. J. Inst. Eng. India Ser. B 93, 163–172 (2012). https://doi.org/10.1007/s40031-012-0020-1
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s40031-012-0020-1