A priori knowledge based deformable surface model for newborn brain MR image segmentation

Abstract

Newborn brain MR image segmentation is a crucial procedure for computer-aided diagnosis of brain disorders using MR images. We have previously proposed an automated method for segmenting parenchymal region. The method is based on a fuzzy rule based deformable surface model. In order to improve the segmentation accuracy, this paper introduces a priori knowledge represented by fuzzy object radial model called FORM. The FORM is generated from learning data set, and represents knowledge on shape and MR signal of parenchymal region in MR images. The performance of the proposed method has been validated by using 12 newborn volunteers whose revised age was between -1 month and 1 month. In comparison with the previous method, the proposed method showed the best performance, and the sensitivity was 87.6 % and false-positive-rate (FPR) was 5.68 %. And, leave-one-out cross validation (LOOCV) was conducted to evaluate the robustness. Mean sensitivity and FPR in LOOCV was 86.7 % and 12.1 %.