The presence of microcalcification clusters (MCs) in mammogram is a major indicator of breast cancer. Detection of an MC is one of the key issues for breast cancer control. In this paper, we present a highly accurate method based on a morphological image processing and wavelet transform technique to detect the MCs in mammograms. The microcalcifications are firstly enhanced by using multistructure elements morphological processing. Then, the candidates of microcalcifications are refined by a multilevel wavelet reconstruction approach. Finally, MCs are detected based on their distributions feature. Experiments are performed on 138 clinical mammograms. The proposed method is capable of detecting 92.9% of true microcalcification clusters with an average of 0.08 false microcalcification clusters detected per image. 1. Introduction Breast cancer is one of the major causes of mortality in middle-aged women, especially in developed countries [1]. At present, there are no effective ways to prevent breast cancer since its cause remains unknown [2]. Therefore, early detection becomes the key to improving the breast cancer prognosis and reducing the mortality rates. Mammography has been widely recognized as being one of the most effective imaging modalities for early detection of breast cancer. However, it is a hard work for radiologists to provide both accurate and uniform evaluation for the enormous number of mammograms generated in widespread screening. A computer-aided detection or diagnosis (CAD) system, which uses computer technologies to detect the typical signs of breast cancer, has been developed to provide a “second opinion” for radiologists and to improve the accuracy and stability of diagnosis. In general, there are three signs of breast cancer in a mammogram: microcalcification clusters (MCs), architectural distortions, and masses [2]. In this paper, we particularly focus on the detection of MCs since they appear in 30–50% of mammographic diagnosed cases and show a high correlation with breast cancer [3]. According to the Breast Image Reporting and Data System (BI-RADS) lexicon [4], MCs are tiny calcium deposits that appear as small bright spots in mammograms. As an example, Figure 1 shows an MC in a mediolateral-oblique (MLO) mammogram. It is often hard for radiologists to find individual MCs in mammograms because they are very small (typically, 0.05–1？mm [3]) in the size and the contrast between the MCs and the surrounding breast tissue is not high enough. Figure 1: An example of an MC. (a) A mediolateral-oblique (MLO) mammogram. (b) Expanded view
References
[1]
R. E. Bird, T. W. Wallace, and B. C. Yankaskas, “Analysis of cancers missed at screening mammography,” Radiology, vol. 184, no. 3, pp. 613–617, 1992.
[2]
J. Tang, R. M. Rangayyan, J. Xu, I. E. El Naqa, and Y. Yang, “Computer-aided detection and diagnosis of breast cancer with mammography: recent advances,” IEEE Transactions on Information Technology in Biomedicine, vol. 13, no. 2, pp. 236–251, 2009.
[3]
D. B. Kopans, Breast Imaging, J. B. Lippincott Company, 1989.
[4]
H. D. Cheng, X. Cai, X. Chen, L. Hu, and X. Lou, “Computer-aided detection and classification of microcalcifications in mammograms: a survey,” Pattern Recognition, vol. 36, no. 12, pp. 2967–2991, 2003.
[5]
M. L. Giger, “Computer-aided diagnosis of breast lesions in medical images,” Computing in Science and Engineering, vol. 2, no. 5, pp. 39–45, 2000.
[6]
N. Karssemeijer and J. H. Hendriks, “Computer-assisted reading of mammograms,” European Radiology, vol. 7, no. 5, pp. 743–748, 1997.
[7]
L. Zhang, R. Sankar, and W. Qian, “Advances in micro-calcification clusters detection inmammography,” Computers in Biology and Medicine, vol. 32, no. 6, pp. 515–528, 2002.
[8]
I. El Naqa and Y. Yang, “Techniques in the detection of microcalcification clusters in digital mammograms,” Medical Imaging Systems Technology: Methods in Diagnosis Optimization, vol. 4, pp. 15–36, 2005.
[9]
R. M. Nishikawa, M. L. Giger, K. Doi, C. J. Vyborny, and R. A. Schmidt, “Computer-aided detection of clustered microcalcifications on digital mammograms,” Medical and Biological Engineering and Computing, vol. 33, no. 2, pp. 174–178, 1995.
[10]
K. J. McLoughlin, P. J. Bones, and N. Karssemeijer, “Noise equalization for detection of microcalcification clusters in direct digital mammogram images,” IEEE Transactions on Medical Imaging, vol. 23, no. 3, pp. 313–320, 2004.
[11]
W. Qian, F. Mao, X. Sun, Y. Zhang, D. Song, and R. A. Clarke, “An improved method of region grouping for microcalcification detection in digital mammograms,” Computerized Medical Imaging and Graphics, vol. 26, no. 6, pp. 361–368, 2002.
[12]
M. G. Linguraru, K. Marias, R. English, and M. Brady, “A biologically inspired algorithm for microcalcification cluster detection,” Medical Image Analysis, vol. 10, no. 6, pp. 850–862, 2006.
[13]
J. Dengler, S. Behrens, and J. F. Desaga, “Segmentation of microcalcifications in mammograms,” IEEE Transactions on Medical Imaging, vol. 12, no. 4, pp. 634–642, 1993.
[14]
G. Lemaur, K. Drouiche, and J. DeConinck, “Highly regular wavelets for the detection of clustered microcalcifications in mammograms,” IEEE Transactions on Medical Imaging, vol. 22, no. 3, pp. 393–401, 2003.
[15]
M. G. Mini, V. P. Devassia, and T. Thomas, “Multiplexed wavelet transform technique for detection of microcalcification in digitized mammograms,” Journal of Digital Imaging, vol. 17, no. 4, pp. 285–291, 2004.
[16]
R. Nakayama, Y. Uchiyama, K. Yamamoto, R. Watanabe, and K. Namba, “Computer-aided diagnosis scheme using a filter bank for detection of microcalcification clusters in mammograms,” IEEE Transactions on Biomedical Engineering, vol. 53, no. 2, pp. 273–283, 2006.
[17]
E. Regentova, L. Zhang, J. Zheng, and G. Veni, “Microcalcification detection based on wavelet domain hidden Markov tree model: study for inclusion to computer aided diagnostic prompting system,” Medical Physics, vol. 34, no. 6, pp. 2206–2219, 2007.
[18]
M. N. Gürcan, Y. Yardimci, A. E. ？etin, and R. Ansari, “Detection of microcalcifications in mammograms using higher order statistics,” IEEE Signal Processing Letters, vol. 4, no. 8, pp. 211–216, 1997.
[19]
B. Caputo, E. L. Torre, S. Bouattour, and G. E. Gigante, “A new kernel method for microcalcification detection: spin Glass-Markov random fields,” Studies in Health Technology and Informatics, vol. 90, pp. 30–34, 2002.
[20]
S. Yu and L. Guan, “A CAD system for the automatic detection of clustered microcalcifications in digitized mammogram films,” IEEE Transactions on Medical Imaging, vol. 19, no. 2, pp. 115–126, 2000.
[21]
J. Jiang, B. Yao, and A. M. Wason, “A genetic algorithm design for microcalcification detection and classification in digital mammograms,” Computerized Medical Imaging and Graphics, vol. 31, no. 1, pp. 49–61, 2007.
[22]
Y. Peng, B. Yao, and J. Jiang, “Knowledge-discovery incorporated evolutionary search for microcalcification detection in breast cancer diagnosis,” Artificial Intelligence in Medicine, vol. 37, no. 1, pp. 43–53, 2006.
[23]
L. Bocchi, G. Coppini, J. Nori, and G. Valli, “Detection of single and clustered microcalcifications in mammograms using fractals models and neural networks,” Medical Engineering and Physics, vol. 26, no. 4, pp. 303–312, 2004.
[24]
M. N. Gurcan, H. P. Chan, B. Sahiner, L. Hadjiiski, N. Petrick, and M. A. Helvie, “Optimal neural network architecture selection: improvement in computerized detection of microcalcifications,” Academic Radiology, vol. 9, no. 4, pp. 420–429, 2002.
[25]
A. Papadopoulos, D. I. Fotiadis, and A. Likas, “An automatic microcalcification detection system based on a hybrid neural network classifier,” Artificial Intelligence in Medicine, vol. 25, no. 2, pp. 149–167, 2002.
[26]
P. Sajda, C. Spence, and J. Pearson, “Learning contextual relationships in mammograms using a hierarchical pyramid neural network,” IEEE Transactions on Medical Imaging, vol. 21, no. 3, pp. 239–250, 2002.
[27]
I. El-Naqa, Y. Yang, M. N. Wernick, N. P. Galatsanos, and R. M. Nishikawa, “A support vector machine approach for detection of microcalcifications,” IEEE Transactions on Medical Imaging, vol. 21, no. 12, pp. 1552–1563, 2002.
[28]
S. Singh, V. Kumar, H. K. Verma, and D. Singh, “SVM based system for classification of microcalcifications in digital mammograms,” in Proceedings of the 28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBS '06), pp. 4747–4750, New York, NY, USA, September 2006.
[29]
L. Wei, Y. Yang, R. M. Nishikawa, M. N. Wernick, and A. Edwards, “Relevance vector machine for automatic detection a of clustered microcalcifications,” IEEE Transactions on Medical Imaging, vol. 24, no. 10, pp. 1278–1285, 2005.
[30]
R. C. Gonzalez and E. W. Richard, Digital Image Processing, Prentice Hall, New York, NY, USA, 3rd edition, 2009.
[31]
I. Daubechies, Ten Lecture on Wavelet, SIAM, Philadelphia, Pa, USA, 1992.
[32]
Y. Kabbadj, F. Regragui, and M. M. Himmi, “Detection of microcalcification in digitized mammograms using weighted local differences and local contrast,” Applied Mathematical Sciences, vol. 6, no. 131, pp. 6533–6544, 2012.
[33]
F. Eddaoudi and F. Regragui, “Microcalcifications detection in mammographic images using texture coding,” Applied Mathematical Sciences, vol. 5, no. 8, pp. 381–393, 2011.
[34]
H. Jing, Y. Yang, and R. M. Nishikawa, “Detection of clustered microcalcifications using spatial point process modeling,” Physics in Medicine and Biology, vol. 56, no. 1, pp. 1–17, 2011.
