Abstract
Tumor grading is as important as difficult to assert, and there is a growing interest in making this possible from the medical images. Very often radiologist deal with the lack of consensus regarding tumor classification and grading. Despite there is an apparently comprehensive guide to qualify the masses, the qualitative nature of the classification directives guides the specialists to different appreciations. Then, the histology dissolves any hesitation at the expense of time and patient discomfort. Having a reliable quantifying strategy would not only assist the radiologist in their verdicts, but it will also speed up the diagnosing processes and may avoid the need for invasive and uncomfortable procedures such as the biopsy. In this manuscript, we present two algorithms that extract numbers in a slice by slice fashion using the tumor edging irregularities. As a proof of concetp, clinical breast tomosythesis images are treated under the mentioned methods and their outcomes are presented next to the extracted numerical insights.
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Yepes-Calderon, F., Ospina, C., Marina, F., Abella, J. (2017). Presenting Two Tumor’ Edge Analyzing Strategies to Assert Lobularity Quantification on Digital Breast Tomosythesis Images. In: Solano, A., Ordoñez, H. (eds) Advances in Computing. CCC 2017. Communications in Computer and Information Science, vol 735. Springer, Cham. https://doi.org/10.1007/978-3-319-66562-7_52
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DOI: https://doi.org/10.1007/978-3-319-66562-7_52
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