Abstract
In mammographic images, the presence of microcalcification clusters is a primary indicator of breast cancer. However, not all microcalcification clusters are malignant and it is difficult and time consuming for radiologists to discriminate between malignant and benign microcalcification clusters. In this paper, a novel method for classifying microcalcification clusters in mammograms is presented. The topology/connectivity of microcalcification clusters is analysed by representing their topological structure over a range of scales in graphical form. Graph theoretical features are extracted from microcalcification graphs to constitute the topological feature space of microcalcification clusters. This idea is distinct from existing approaches that tend to concentrate on the morphology of individual microcalcifications and/or global (statistical) cluster features. The validity of the proposed method is evaluated using two well-known digitised datasets (MIAS and DDSM) and a full-field digital dataset. High classification accuracies (up to 96%) and good ROC results (area under the ROC curve up to 0.96) are achieved. In addition, a full comparison with state-of-the-art methods is provided.
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References
Shen, L., et al.: Application of Shape Analysis to Mammographic Calcifications. IEEE Transactions on Medical Imaging 13(2), 263–274 (1994)
Dhawan, A.P., et al.: Analysis of Mammographic Microcalcifications Using Gray-level Image Structure Features. IEEE Transactions on Medical Imaging 15(3), 246–259 (1996)
Cheng, H.D., et al.: Computer-Aided Detection and Classification of Microcalcifications in Mammograms: A Survey. Pattern Recognition 36(12), 2967–2991 (2003)
Ma, Y., et al.: A Novel Shape Feature to Classify Microcalcifications. In: IEEE 17th International Conference on Image Processing, pp. 2265–2268 (2010)
Soltanian-Zadeh, H., et al.: Comparison of Multiwavelet, Wavelet, Haralick, and Shape Features for Microcalcification Classification in Mammograms. Pattern Recognition 37(10), 1973–1986 (2004)
Sickles, E.A.: Breast Calcifications: Mammographic Evaluation. Radiology 160(2), 289–293 (1986)
Dähnert, W.: Radiology Review Manual, 6th edn. Lippincott Williams & Wilkins, Philadelphia (2007)
Papadopoulos, A., et al.: Characterization of Clustered Microcalcifications in Digitized Mammograms Using Neural Networks and Support Vector Machines. Artificial Intelligence in Medicine 34(2), 141–150 (2005)
Elter, M., Horsch, A.: CADx of mammographic masses and clustered microcalcifications: A review. Medical Physics 36, 2052–2068 (2009)
Chen, Z., Denton, E.R.E., Zwiggelaar, R.: Classification of Microcalcification Clusters Based on Morphological Topology Analysis. In: Maidment, A.D.A., Bakic, P.R., Gavenonis, S. (eds.) IWDM 2012. LNCS, vol. 7361, pp. 521–528. Springer, Heidelberg (2012)
Betal, D., et al.: Segmentation and numerical analysis of microcalcifications on mammograms using mathematical morphology. British Journal of Radiology 70, 903–917 (1997)
Chan, H.P., et al.: Computerized classification of malignant and benign microcalcifications on mammograms: Texture analysis using an artificial neural network. Physics in Medicine and Biology 42, 549–567 (1997)
Rana, R.S., et al.: Independent evaluation of computer classification of malignant and benign calcifications in full-field digital mammograms1. Academic Radiology 14, 363–370 (2007)
Ren, J., et al.: Effective recognition of MCCs in mammograms using an improved neural classifier. Engineering Applications of Artificial Intelligence 24, 638–645 (2011)
Ren, J.: ANN vs. SVM: Which one performs better in classification of MCCs in mammogram imaging. Knowledge-Based Systems 26, 144–153 (2012)
Dheeba, J., Selvi, S.T.: A Swarm Optimized Neural Network System for Classification of Microcalcification in Mammograms. Journal of Medical Systems 36, 3051–3061 (2012)
Wei, L., et al.: Microcalcification classification assisted by content-based image retrieval for breast cancer diagnosis. Pattern Recognition 42, 1126–1132 (2009)
Feig, S.A., et al.: Evaluation of breast microcalcifications by means of optically magnified tissue specimen radiographs. Recent Results in Cancer Research 105, 111–123 (1987)
Oliver, A., et al.: Automatic microcalcification and cluster detection for digital and digitised mammograms. Knowledge-Based Systems 28, 68–75 (2012)
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Chen, Z., Strange, H., Denton, E., Zwiggelaar, R. (2014). Analysis of Mammographic Microcalcification Clusters Using Topological Features. In: Fujita, H., Hara, T., Muramatsu, C. (eds) Breast Imaging. IWDM 2014. Lecture Notes in Computer Science, vol 8539. Springer, Cham. https://doi.org/10.1007/978-3-319-07887-8_86
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DOI: https://doi.org/10.1007/978-3-319-07887-8_86
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-07886-1
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