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.
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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 [6] for agreeing to our use of yet unpublished data.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.
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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
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DOI: https://doi.org/10.1007/978-3-319-41546-8_73
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