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2020 | OriginalPaper | Buchkapitel

A Novel Application of Multifractal Features for Detection of Microcalcifications in Digital Mammograms

verfasst von : Haipeng Li, Ramakrishnan Mukundan, Shelley Boyd

Erschienen in: Medical Image Understanding and Analysis

Verlag: Springer International Publishing

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Abstract

This paper presents a novel image processing algorithm for automated microcalcifications (MCs) detection in digital mammograms. In order to improve the detection accuracy and reduce false positive (FP) numbers, two scales of sub-images are considered for detecting varied sized MCs, and different processing algorithms are used on them. The main contributions of this research work include: use of multifractal analysis based methods to analyze mammograms and describe MCs texture features; development of adaptive α values selection rules for better highlighting MCs patterns in mammograms; application of an effective SVM classifier to predict the existence of tiny MC spots. A full-field digital mammography (FFDM) dataset INbreast is used to test our proposed method, and experimental results demonstrate that our detection algorithm outperforms other reported methods, reaching a higher sensitivity (80.6%) and reducing FP numbers to lower levels.

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WHO. http://www.who.int/cancer/detection/breastcancer/en/index3.html. Accessed 11 Dec 2018

Breast Cancer Foundation New Zealand. https://www.breastcancerfoundation.org.nz/breast-awareness/breast-cancer-facts/breast-cancer-in-nz. Accessed 30 Jan 2019

Sampat, M.P., Bovik, A.C., Whitman, G.J.: A model-based framework for the detection of speculated masses on mammography. Med. Phys. 35, 2110–2123 (2008)CrossRef

Singh, B., Kaur, M.: An approach for classification of malignant and benign microcalcification clusters. Sadhana-Acad. Proc. Eng. Sci. 43(3) (2018). https://doi.org/10.1007/s12046-018-0805-2

Guo, Y.N., et al.: A new method of detecting micro-calcification clusters in mammograms using contourlet transform and non-linking simplified PCNN. Comput. Methods Programs Biomed. 130, 31–45 (2016). https://doi.org/10.1016/j.cmpb.2016.02.019CrossRef

Mehdi, M.Z., Ben Ayed, N.G., Masmoudi, A.D., Sellami, D., Abid, R.: An efficient microcalcifications detection based on dual spatial/spectral processing. Multimedia Tools Appl. 76(11), 13047–13065 (2017). https://doi.org/10.1007/s11042-016-3703-9CrossRef

Ciecholewski, M.: Microcalcification segmentation from mammograms: a morphological approach. J. Digit. Imaging 30(2), 172–184 (2017). https://doi.org/10.1007/s10278-016-9923-8CrossRef

Liu, X.M., Mei, M., Liu, J., Hu, W.: Microcalcification detection in full-field digital mammograms with PFCM clustering and weighted SVM-based method. Eurasip J. Adv. Signal Process. 1–13 (2015). https://doi.org/10.1186/s13634-015-0249-3

Ibrahim, M., Mukundan, R.: Multifractal techniques for emphysema classification in lung tissue images. In: 3rd International Conference on Future Bioengineering (ICFB), pp. 115–119 (2014)

10.

Ibrahim, M., Mukundan, R.: Cascaded techniques for improving emphysema classification in CT images. Artif. Intell. Res. 4(2), 112–118 (2015). https://doi.org/10.5430/air.v4n2p112

11.

Sahli, I.S., Bettaieb, H.A., Ben Abdallah, A., Bhouri, I., Bedoui, M.H.: Detection and segmentation of microcalcifications in digital mammograms using multifractal analysis. In: 5th International Conference on Image Processing, Theory, Tools and Applications 2015, pp. 180–184 (2015)

12.

Stojic, T., Rejin, I., Rejin, B.: Adaptation of multifractal analysis to segmentation of microcalcifications in digital mammograms. Phys. A-Stat. Mech. Appl. 367, 494–508 (2006). https://doi.org/10.1016/j.physa.2005.11.030CrossRef

13.

Moreira, I.C., Amaral, I., Domingues, I., Cardoso, A., Cardoso, M.J., Cardoso, J.S.: INbreast: toward a full-field digital mammographic database. Acad. Radiol. 19(2), 236–248 (2012). https://doi.org/10.1016/j.acra.2011.09.01414

14.

Suckling, J., et al.: Mammographic Image Analysis Society (MIAS) database v1.21 [Dataset] (2015). https://www.repository.cam.ac.uk/handle/1810/250394

15.

Shi, P., Zhong, J., Rampun, A., Wang, H.: A hierarchical pipeline for breast boundary segmentation and calcification detection in mammograms. Comput. Biol. Med. 96, 178–188 (2018). https://doi.org/10.1016/j.compbiomed.2018.03.011CrossRef

16.

Rampun, A., Morrow, P.J., Scotney, B.W., Winder, J.: Fully automated breast boundary and pectoral muscle segmentation in mammograms. Artif. Intell. Med. 79, 28–41 (2017). https://doi.org/10.1016/j.artmed.2017.06.001CrossRef

17.

Braverman, B., Tambasco, M.: Scale-specific multifractal medical image analysis. Comput. Math. Methods Med. (2013). https://doi.org/10.1155/2013/262931MathSciNetCrossRef

18.

Falconer, K.: Random Fractals. Fractal Geometry: Mathematical Foundations and Applications, 2nd edn. Wiley, Chichester (2005)

19.

Reljin, I., Reljin, B., Pavlovic, I., Rakocevic, I.: Multifractal analysis of gray-scale images. In: MELECON 2000: Information Technology and Electrotechnology for the Mediterranean Countries, vol. 1–3, Proceedings, pp. 490–493 (2000)

20.

Wang, J., Yang, Y.Y., Nishikawa, R.M.: Reduction of false positive detection in clustered microcalcifications. In: 2013 20th IEEE International Conference on Image Processing (ICIP 2013), pp. 1433–1437 (2013)

21.

Zwiggelaar, R., Astley, S.M., Boggis, C.R.M., Taylor, C.J.: Linear structures in mammographic images: detection and classification. IEEE Trans. Med. Imaging 23(9), 1077–1086 (2004). https://doi.org/10.1109/Tmi.2004.828675CrossRef

22.

Sickles, E.A., D’Orsi, C.J., Bassett, L.W., et al.: ACR BI-RADS® mammography. In: ACR BI-RADS® Atlas, Breast Imaging Reporting and Data System. American College of Radiology, Reston (2013)

23.

Domingues, I., Cardoso, J.S.: Using Bayesian surprise to detect calcifications in mammogram images. In: 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 1091–1094 (2014)

24.

Zhang, E.H., Wang, F., Li, Y.C., Bai, X.N.: Automatic detection of microcalcifications using mathematical morphology and a support vector machine. Bio-Med. Mater. Eng. 24(1), 53–59 (2014). https://doi.org/10.3233/Bme-130783CrossRef

Titel: A Novel Application of Multifractal Features for Detection of Microcalcifications in Digital Mammograms
verfasst von: Haipeng Li
Ramakrishnan Mukundan
Shelley Boyd
Verlag: Springer International Publishing
Buch: Medical Image Understanding and Analysis
Print ISBN: 978-3-030-39342-7

Electronic ISBN: 978-3-030-39343-4

Copyright-Jahr: 2020
DOI: https://doi.org/10.1007/978-3-030-39343-4_3

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