Staging liver fibrosis by analysis of non-linear normalization texture in gadolinium-enhanced magnetic resonance imaging

Purpose: A reliable non-invasive approach for accurate staging fibrosis is still under development. In this study, we utilized a computer aided diagnosis (CAD) system to analyze equilibrium phase magnetic resonance (MR) images in a rat model to determine relative accuracy of staging liver fibrosis with CAD. Methods: Experimental rats were injected with a mixture of CCl4 to generate varying stages of fibrosis and equilibrium phase images of rats were acquired. All rats were grouped based on histological stages (F0, F1, F2, F3 and F4). During the process of CAD, the first step was manual section of region of interest (ROI) with 10*10 pixels guided by histology. Next, Lloyd's algorithm and linear normalization were both used to compress ROIs to 256 gray level. Then, 80 texture features based on the gray level co-occurrence matrix were extracted. Lastly, the back-propagation (BP), linear, k-nearest neighbor and support vector machine classifiers were individually used to classify F0-F4. Results: With histological staging as a reference, we found that performance of the BP classifier based on Lloyd's algorithm in staging fibrosis was better than that of linear normalization. Staging accuracy rates of the former were 67.27%, 80.26% and 79.41% for F0 versus F3, F2 versus F4 and F3 versus F4, respectively, secondary to none of the other classifiers. Conclusions: The assessment of equilibrium phase MR images with CAD can effectively stage rat liver fibrosis. This article highlights progress in developing a reliable non-invasive liver fibrosis staging procedure.