Abstract
The density of a significant number of liver tumors is not very contrasting from the density of surrounding liver tissue. Therefore, the use of conventional automatic segmentation methods did not provide satisfactory results. Thresholding-based algorithms are not able to detect the right threshold value, region growing algorithms cannot deal with the noisy-nature of data etc. Even more advanced algorithms cannot deal with such problems. For example, the active contours are often leaking out from the tumorous area due to the low contrast at its border. Thus a new fully automatic approach was designed that is based on the saliency maps and the Markov random fields. This approach can deal with the nature of medical data and is able to localize liver lesions with satisfactory precision.
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The work has been supported by the project “Sustainability support of the centre NTIS – New Technologies for the Information Society”, LO1506.
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Ryba, T., Železný, M. (2018). Saliency Maps for Localization of Liver Lesions. In: Tavares, J., Natal Jorge, R. (eds) VipIMAGE 2017. ECCOMAS 2017. Lecture Notes in Computational Vision and Biomechanics, vol 27. Springer, Cham. https://doi.org/10.1007/978-3-319-68195-5_41
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DOI: https://doi.org/10.1007/978-3-319-68195-5_41
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