Abstract
In this paper we present a set of 3D-rigid motion invariant texture features. We experimentally establish that when they are combined with mean attenuation intensity differences the new augmented features are capable of discriminating normal from abnormal liver tissue in arterial phase contrast enhanced X-ray CT–scans with high sensitivity and specificity. To extract these features CT-scans are processed in their native dimensionality. We experimentally observe that the 3D-rotational invariance of the proposed features improves the clustering of the feature vectors extracted from normal liver tissue samples.
This work was partially supported by NSF DMS 0915242 award.
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Upadhyay, S., Papadakis, M., Jain, S., Gladish M.D., G., Kakadiaris, I.A., Azencott, R. (2012). Semi-automatic Discrimination of Normal Tissue and Liver Cancer Lesions in Contrast Enhanced X-Ray CT-Scans. In: Yoshida, H., Hawkes, D., Vannier, M.W. (eds) Abdominal Imaging. Computational and Clinical Applications. ABD-MICCAI 2012. Lecture Notes in Computer Science, vol 7601. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33612-6_17
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DOI: https://doi.org/10.1007/978-3-642-33612-6_17
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