The Research of Reproducibility and Non-redundancy Feature Selection Methods in Radiomics

Bing-Yan WEI, Jian-Lin SONG, Li-Xu GU
2017 DEStech Transactions on Computer Science and Engineering  
Radiomics" is a process of extracting a great quantity of descriptive features from biomedical images that can be used for prognosis. One of the major challenges of radiomics is to acquire the features that reproducibility and non-redundancy. The reproducibility of features dependent on the segmentation algorithm, and it should provide an accurate and reproducible result. In this study, a semi-automated segmentation method based on, which has an good effect for gray scale inhomogeneous of
more » ... was used to get tumor region for computed tomographic (CT) images of 35 non-small cell lung cancer (NSCLC) patients. A set of features (125 3D and 92 2D) was computed for each tumor region in the test/retest data set. In terms of comparing the intra-class correlation coefficient (ICC) with manual segmentation method in feature extracting, it is indicated that the features obtained by the method of on CV model has a better representative. A series quantitative feature of better reproducibility could be obtained. However, these features were redundant. A feature selection method based on sparse representation coefficient (SRC) was used to filter these redundant features. It is indicated that the features obtained by SRC have better non-redundancy through comparison the selection methods based on Pearson correlation coefficient (PCC) and symmetrical uncertainty (SU). Thus quantitative image features that reproducibility and non-redundancy provide informative and prognostic biomarkers for NSCLC.
doi:10.12783/dtcse/aice-ncs2016/5661 fatcat:jx7zla2gkbh53b5bh736ghc5hm