Evaluation of texture features at staging liver fibrosis based on phase contrast X-ray imaging

BioMedical Engineering OnLine

Ming Wang¹  Jing Wang¹  Song Gao¹  Hui Li¹ 

[1] Department of Medical Physics, School of Foundational Education, Peking University Health Science Center;

关键词: Liver fibrosis;    Mouse liver specimen;    Phase contrast imaging;    Texture features;    Neural network;   

DOI  :  10.1186/s12938-018-0612-3

来源: DOAJ

【 摘 要 】

Abstract Background The purpose of this study is to explore the potential of phase contrast imaging to detect fibrotic progress in its early stage; to investigate the feasibility of texture features for quantified diagnosis of liver fibrosis; and to evaluate the performance of back propagation (BP) neural net classifier for characterization and classification of liver fibrosis. Methods Fibrous mouse liver samples were imaged by X-ray phase contrast imaging, nine texture measures based on gray-level co-occurrence matrix were calculated and the feasibility of texture features in the characterization and discrimination of liver fibrosis at early stages was investigated. Furthermore, 36 or 18 features were applied to the input of BP classifier; the classification performance was evaluated using receiver operating characteristic curve. Results The phase contrast images displayed a vary degree of texture pattern from normal to severe fibrosis stages. The BP classifier could distinguish liver fibrosis among normal, mild, moderate and severe stages; the average accuracy was 95.1% for 36 features, and 91.1% for 18 features. Conclusion The study shows that early stages of liver fibrosis can be discriminated by the morphological features on the phase contrast images. BP network model based on combination of texture features is demonstrated effective for staging liver fibrosis.

【 授权许可】

Unknown