Abstract #5072

Wavelet based Texture Analysis of Liver Fibrosis in Delayed Phase Gadolinium-Enhanced T1-weighted in vivo Images

Lavanya Umapathy¹, Jonathan Brand², Jean-Philippe Galons³, Lars Furenlid^2,3, Diego Martin³, Maria Altbach³, and Ali Bilgin^1,4

¹Electrical and Computer Engineering, University of Arizona, Tucson, AZ, United States, ²College of Optical Sciences, University of Arizona, Tucson, AZ, United States, ³Medical Imaging, University of Arizona, Tucson, AZ, United States, ⁴Biomedical Engineering, University of Arizona, Tucson, AZ, United States

Non-invasive imaging techniques that can identify early structural changes due to fibrosis in vivo are of high clinical importance. In this work, a five-level wavelet decomposition of biopsy confirmed normal and fibrotic ex vivo liver tissues is performed and histogram-based features are extracted from the wavelet subbands. A linear classifier is trained using the top 10 features and applied to classify liver fibrosis in Gadolinium-enhanced delayed phase T1-weighted in vivo images. The results show that normal samples yield low posterior probabilities for fibrosis whereas these values are very high for fibrotic samples.

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