Automatic detection of microcalcification based on morphological operations and structural similarity indices

• Autorzy: Touil Asma , Kalti Karim , Conze Pierre-Henri , Solaiman Basel , Mahjoub Mohamed Ali

Identyfikatory

DOI

10.1016/j.bbe.2020.05.002

• Języki publikacji: EN

Abstrakty

EN

In this paper, a new method for automatic detection of microcalcifications in digitized mammograms is proposed. Based on mathematical morphology theory to deal with the problem of low contrast between microcalcifications and their surrounding pixels, it uses various structuring elements of different sizes to reduce the sensibility to microcalcification diversity sizes. The obtained morphological results are converted to a suspicion map based on an image quality assessment metric called structural similarity index (SSIM). This continuous map is, then, locally analyzed using superpixels to automatically estimate threshold values and finally detect potential microcalcification areas. The proposed method was evaluated using the publiclyavailable INBreast dataset. Experimental results show the benefits gained in terms of improving microcalcification detection performances compared to state-of-the-art methods.

Słowa kluczowe

EN

mammography; breast cancer; microcalcification detection; mathematical morphology; structural similarity index

PL

mammografia; rak piersi; morfologia matematyczna; podobieństwo strukturalne

• Wydawca: Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences ; Elsevier
• Czasopismo: Biocybernetics and Biomedical Engineering
• Rocznik: 2020
• Tom: Vol. 40, no. 3
• Strony: 1155--1173
• Opis fizyczny: Bibliogr. 47 poz., rys., tab.

Twórcy

autor

Touil Asma

• Université de Sousse, Ecole Nationale d'Ingénieurs de Sousse, LATIS-Laboratory of Advanced Technology and Intelligent Systems, Sousse, Tunisia; Université de Sousse, Institut Supérieur d'Informatique et des Techniques de Communication, Hammam Sousse, Tunisia; IMT Atlantique, LaTIM UMR 1101, UBL, Brest, France

autor

Kalti Karim

• Université de Sousse, Ecole Nationale d'Ingénieurs de Sousse, LATIS-Laboratory of Advanced Technology and Intelligent Systems, Sousse, Tunisia; Université de Sousse, Institut Supérieur d'Informatique et des Techniques de Communication, Hammam Sousse, Tunisia

autor

Conze Pierre-Henri

• IMT Atlantique, LaTIM UMR 1101, UBL, Brest, France

autor

Solaiman Basel

• IMT Atlantique, LaTIM UMR 1101, UBL, Brest, France

autor

Mahjoub Mohamed Ali

• Université de Sousse, Ecole Nationale d'Ingénieurs de Sousse, LATIS-Laboratory of Advanced Technology and Intelligent Systems, Sousse, Tunisia; Université de Sousse, Institut Supérieur d'Informatique et des Techniques de Communication, Hammam Sousse, Tunisia

Bibliografia

• [1] Nounou MI, ElAmrawy F, Ahmed N, Abdelraouf K, Goda S, Syed-Sha-Qhattal H. Breast cancer: conventional diagnosis and treatment modalities and recent patents and technologies. Breast Cancer Basic Clin Res 2015;9. BCBCR-S29420.
• [2] George M, Chen Z, Zwiggelaar R. Multiscale connected chain topological modelling for microcalcification classification. Comput Biol Med 2019;114:103422.
• [3] Sapate S, Talbar S, Mahajan A, Sable N, Desai S, Thakur M. Breast cancer diagnosis using abnormalities on ipsilateral views of digital mammograms. Biocybern Biomed Eng 2020;40:290–305.
• [4] Wang J, Yang X, Cai H, Tan W, Jin C, Li L. Discrimination of breast cancer with microcalcifications on mammography by deep learning. Sci Rep 2016;6:27327.
• [5] Ali MA, Czene K, Hall P, Humphreys K. Association of microcalcification clusters with short-term invasive breast cancer risk and breast cancer risk factors. Sci Rep 2019;9:1–8.
• [6] Rodríguez-Ruiz A, Krupinski E, Mordang J-J, Schilling K, Heywang-K“obrunner SH, Sechopoulos I, et al. Detection of breast cancer with mammography: effect of an artificial intelligence support system. Radiology 2018;290:305–14.
• [7] Singh BK. Determining relevant biomarkers for prediction of breast cancer using anthropometric and clinical features: a comparative investigation in machine learning paradigm. Biocybern Biomed Eng 2019;39:393–409.
• [8] Bonfiglio R, Scimeca M, Urbano N, Bonanno E, Schillaci O. Breast Microcalcifications: Biological and Diagnostic Perspectives; 2018.
• [9] Albiol A, Corbi A, Albiol F. Automatic intensity windowing of mammographic images based on a perceptual metric. Med Phys 2017;44:1369–78.
• [10] Valvano G, Della Latta D, Martini N, Santini G, Gori A, Iacconi C, et al. Evaluation of a deep convolutional neural network method for the segmentation of breast microcalcifications in mammography imaging. EMBEC & NBC 2017. Springer; 2017. p. 438–41. http://dx.doi.org/10.1007/978-981-10-5122-7.
• [11] Valvano G, Santini G, Martini N, Ripoli A, Iacconi C, Chiappino D, et al. Convolutional neural networks for the segmentation of microcalcification in mammography imaging. J Healthc Eng 2019;2019.
• [12] Betal D, Roberts N, Whitehouse G. Segmentation and numerical analysis of microcalcifications on mammograms using mathematical morphology. Br J Radiol 1997;70:903–17.
• [13] Ciecholewski M. Microcalcification segmentation from mammograms: a morphological approach. J Digit Imaging 2016;1–13.
• [14] Zhang E, Wang F, Li Y, Bai X. Automatic detection of microcalcifications using mathematical morphology and a support vector machine. Bio-med Mater Eng 2014;24:53–9.
• [15] Malek AA, Rahman WEZWA, Ibrahim A, Mahmud R, Yasiran SS, Jumaat AK. Region and boundary segmentation of microcalcifications using seed-based region growing and mathematical morphology. Procedia Soc Behav Sci 2010;8:634–9.
• [16] Touil A, Kalti K, Solaiman B, Mahjoub MA. Microcalcifications detection from mammographie images based on region growing and variational energy convergence. 4th International Conference on Advanced Technologies for Signal and Image Processing, ATSIP 2018, Sousse, Tunisia, March 21–24, 2018. 2018. pp. 1–6. http://dx.doi.org/10.1109/ATSIP.2018.8364464.
• [17] Duarte MA, Alvarenga AV, Azevedo CM, Calas MJG, Infantosi AF, Pereira WC. Evaluating geodesic active contours in microcalcifications segmentation on mammograms. Comput Methods Progr Biomed 2015;122:304–15.
• [18] Quintanilla-Domínguez J, Ojeda-Magaña B, Marcano- Cedeño A, Barrón-Adame J, Vega-Corona A, Andina D. Automatic detection of microcalcifications in ROI images based on PFCM and ANN. Int J Intell Comput Med Sci Image Process 2013;5:161–74.
• [19] Veni G, Regentova E, Zhang L. Detection of clustered microcalcifications with Susan edge detector, adaptive contrast thresholding and spatial filters. Image Anal Recognit. Springer; 2008. p. 837–43.
• [20] Kalra PK, Kumar N, et al. A novel automatic microcalcification detection technique using Tsallis entropy & a type ii fuzzy index. Comput Math Appl 2010;60:2426–32.
• [21] Suhail Z, Sarwar M, Murtaza K. Automatic detection of abnormalities in mammograms. BMC Med Imaging 2015;15:53.
• [22] Basile T, Fanizzi A, Losurdo L, Bellotti R, Bottigli U, Dentamaro R, et al. Microcalcification detection in full-field digital mammograms: a fully automated computer-aided system. Phys Med 2019;64:1–9.
• [23] Bria A, Marrocco C, Galdran A, Campilho A, Marchesi A, Mordang J-J, et al. Spatial enhancement by dehazing for detection of microcalcifications with convolutional nets. International Conference on Image Analysis and Processing. Springer; 2017. p. 288–98. http://dx.doi.org/10.1007/978-3-319-68548-9_27.
• [24] Mordang J-J, Gubern-Mérida A, Bria A, Tortorella F, Heeten G, Karssemeijer N. Improving computer-aided detection assistance in breast cancer screening by removal of obviously false-positive findings. Med Phys 2017;44:1390–401.
• [25] Wahab N, Khan A, Lee YS. Two-phase deep convolutional neural network for reducing class skewness in histopathological images based breast cancer detection. Comput Biol Med 2017;85:86–97.
• [26] Hu K, Yang W, Gao X. Microcalcification diagnosis in digital mammography using extreme learning machine based on hidden Markov tree model of dual-tree complex wavelet transform. Expert Syst Appl 2017.
• [27] Wang J, Yang Y. A context-sensitive deep learning approach for microcalcification detection in mammograms. Pattern Recognit 2018;78:12–22.
• [28] Cai H, Huang Q, Rong W, Song Y, Li J, Wang J, et al. Breast microcalcification diagnosis using deep convolutional neural network from digital mammograms. Comput Math Methods Med 2019;2019.
• [29] Zhang F, Luo L, Sun X, Zhou Z, Li X, Yu Y, et al. Cascaded generative and discriminative learning for microcalcification detection in breast mammograms. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition; 2019. p. 12578–86.
• [30] Cruz-Bernal A, Flores-Barranco MM, Almanza-Ojeda DL, Ledesma S, Ibarra-Manzano MA. Analysis of the cluster prominence feature for detecting calcifications in mammograms. J Healthc Eng 2018;2018.
• [31] Cheddad A, Czene K, Shepherd JA, Li J, Hall P, Humphreys K. Enhancement of mammographic density measures in breast cancer risk prediction. Cancer Epidemiol Biomark Prev 2014. cebp-1240.
• [32] Bharadwaj AS, Celenk M. Detection of microcalcification with top-hat transform and the Gibbs random fields. Engineering in Medicine and Biology Society (EMBC), 2015 37th Annual International Conference of the IEEE. IEEE; 2015. p. 6382–5. http://dx.doi.org/10.1109/embc.2015.7319853.
• [33] Gurcan MN, Mc YY, Cetin AE. Microcalcification segmentation and mammogram image enhancement using nonlinear filtering; 1999.
• [34] Bhanumathi R, Suresh G. Detection of microcalcification in mammogram images using support vector machine based classifier. TSI Trans Electr Electron Eng (ITSI-TEEE) 2013;1:27–32.
• [35] Cai H, Yang Z, Cao X, Xia W, Xu X. A new iterative triclass thresholding technique in image segmentation. IEEE Trans Image Process 2014;23:1038–46.
• [36] Balakumaran T, Vennila I, Shankar CG. Detection of microcalcification in mammograms using wavelet transform and fuzzy shell clustering. Int J Comput Sci Inf Secur (IJCSIS) 2010;7:121–5.
• [37] Vignesh WB, Sundaram M. Effect of Contourlet transform in detect of microcalcification in noisy environment. IEEE Sponsored 9th International Conference on Intelligent Systems and Control (ISCO) 2015, at Coimbatore; 2015.
• [38] Najman L, Talbot H. Mathematical morphology: from theory to applications. Wiley; 2013. http://dx.doi.org/10.1002/9781118600788.
• [39] Wang Z, Bovik AC, Sheikh HR, Simoncelli EP, et al. Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process 2004;13:600–12.
• [40] Renieblas GP, Nogués AT, González AM, León NG, Del Castillo EG. Structural similarity index family for image quality assessment in radiological images. J Med Imaging 2017;4:035501.
• [41] Liu W. Conflict analysis and merging operators selection in possibility theory. European Conference on Symbolic and Quantitative Approaches to Reasoning and Uncertainty. Springer; 2007. p. 816–27.
• [42] Ren X, Malik J. Learning a classification model for segmentation. Proceedings Ninth IEEE International Conference on Computer Vision. IEEE; 2003. p. 10. https://ttic.uchicago.edu/xren/publication/ xren_iccv03_discrim.pdf.
• [43] Achanta R, Shaji A, Smith K, Lucchi A, Fua P, S” usstrunk S. Slic superpixels. Technical Report; 2010.
• [44] Seo S. A review and comparison of methods for detecting outliers in univariate data sets [Ph.D. thesis]. University of Pittsburgh; 2006, http://d-scholarship.pitt.edu/ 7948/1/Seo.pdf.
• [45] Moreira IC, Amaral I, Domingues I, Cardoso A, Cardoso MJ, Cardoso JS. Inbreast: toward a full-field digital mammographic database. Acad Radiol 2012;19:236–48.
• [46] Meléndez EL, Urcid G. Mammograms calcifications segmentation based on band-pass Fourier filtering and adaptive statistical thresholding. Eur Int J Sci Technol 2016.
• [47] Alsheh Ali M, Eriksson M, Czene K, Hall P, Humphreys K. Detection of potential microcalcification clusters using multivendor for-presentation digital mammograms for short-term breast cancer risk estimation. Med Phys 2019;46:1938–46.

Uwagi

PL

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).