Abstract
A texture analysis aimed at finding correlations between textural descriptors and lesion diagnosis was applied to Contrast-Enhanced Digital Mammography (CEDM) subtracted images acquired under single-energy temporal subtraction modality using iodine-based contrast medium. The study, based on textural descriptors from Gray Level Co-occurrence Matrix (GLCM), included 68 CEDM images of 17 patients, 10 cancer and 7 benign, acquired 1 to 5 min after iodine injection. Seventeen GLCM descriptors were analyzed. Image processing consisted of geometric registration, logarithmic subtraction, and selection of regions-of-interest (adipose, glandular and lesion ROIs) by the radiologist. Results for lesion ROIs showed that homogeneity, normalized homogeneity, second-order inverse moment, energy and inverse variance were insensitive to the presence of iodine; a linear correlation existed between the sum mean and mean pixel value. Logistic regression showed that a linear combination of entropy and diagonal momentum discriminated between malignant and benign lesions with 79 % specificity, 93 % sensitivity and 87 % accuracy.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Dromain, C., Balleyguier, C., Muller, S., Mathieu, M.C., Rochard, F., Opolon, P., Sigal, R.: Evaluation of tumor angiogenesis of breast carcinoma using contrast-enhanced digital mammography. Am. J. Roentgenol. 187, W528–W537 (2006)
Cruz-Bastida, J.-P., Rosado-Méndez, I., Pérez-Ponce, H., Villaseñor, Y., Galván, H.A., Trujillo-Zamudio, F.E., Benítez-Bribiesca, L., Brandan, M.-E.: Contrast optimization in clinical contrast-enhanced digital mammography images. In: Maidment, A.D., Bakic, P.R., Gavenonis, S. (eds.) IWDM 2012. LNCS, vol. 7361, pp. 17–23. Springer, Heidelberg (2012)
Rosado-Méndez, I., Palma, B.A., Brandan, M.E.: Analytical optimization of digital subtraction mammography with contrast medium using a commercial unit. Med. Phys. 35, 5544–5557 (2008)
Smith, A.: The principles of contrast mammography. Hologic White Paper WP-00084-001 (2014). http://wwww.hologic.com
Diekmann, F., Freyer, M., Diekmann, S., Fallenberg, E.M., Fischer, T., Bick, U., Poellinger, A.: Evaluation of contrast-enhanced digital mammography. Eur. J. Radiol. 78, 112–121 (2011)
Brandan, M.E., Cruz-Bastida, J.P., Rosado-Méndez, I.M., Villaseñor-Navarro, Y., Pérez-Ponce, H., Galván, H.A., Trujillo-Zamudio, F.E., Sánchez-Suárez, P., Benítez-Bribiesca, L.: A clinical study of contrast-enhanced digital mammography for dual-energy and temporal subtraction and the evaluation of blood and lymphatic angiogenesis, March 2016. (in revision)
Chan, H.P., Wei, D., Helvie, M.A., Sahiner, B., Adler, D.D., Goodsitt, M.M., Petrick, N.: Computer-aided classification of mammographic masses and normal tissue: linear discriminant analysis in texture feature space. Phys. Med. Biol. 40, 857–876 (1995)
Lyra, M., Lyra, S., Kostakis, B., Drosos, S., Georgosopoulos, C., Skouroliakou, K.: Digital mammography texture analysis by computer assisted image processing. In: IEEE International Workshop on Imaging Systems and Techniques, pp. 73–76 (2008)
Evangelidis, G.D., Psarakis, E.Z.: Parametric image alignment using enhanced correlation coefficient maximization. IEEE Trans. Pattern Anal. 30, 1858–1865 (2008)
Acknowledgements
We thank authors of Ref. [6] for agreeing to our use of yet unpublished data. UNAM-DGAPA IN105813 and IN107916 supported this work. We thank H Larreguy, ME Martínez and IM Rosado-Méndez for enriching discussions.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Mateos, MJ., Gastelum, A., Márquez, J., Brandan, ME. (2016). Texture Analysis of Contrast-Enhanced Digital Mammography (CEDM) Images. In: Tingberg, A., Lång, K., Timberg, P. (eds) Breast Imaging. IWDM 2016. Lecture Notes in Computer Science(), vol 9699. Springer, Cham. https://doi.org/10.1007/978-3-319-41546-8_73
Download citation
DOI: https://doi.org/10.1007/978-3-319-41546-8_73
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-41545-1
Online ISBN: 978-3-319-41546-8
eBook Packages: Computer ScienceComputer Science (R0)