Age estimation using cortical surface pattern combining thickness with curvatures
Brain development and healthy aging have been proved to follow a specific pattern, which, in turn, can be applied to help doctors diagnose mental diseases. In this paper, we design a cortical surface pattern (CSP) combining the cortical thickness with curvatures, which constructs an accurate human age estimation model with relevance vector regression. We test our model with two public databases. One is the IXI database (360 healthy subjects aging from 20 to 82years old were selected), and the other is the INDI database (303 subjects aging from 7 to 22years old were selected). The results show that our model can achieve as small as 4.57years deviation in the IXI database and 1.38years deviation in the INDI database. Furthermore, we employ this surface pattern to age groups classification and get a remarkably high accuracy (97.77%) and a significantly high sensitivity/specificity (97.30/98.10%). These results suggest that our designed CSP combining thickness with curvatures is stable and sensitive to brain development, and it is much more powerful than voxel-based morphometry used in previous methods for age estimation.
age estimation cortical surface pattern thickness curvatures