Fuzzy object growth model for newborn brain using Manifold learning

To develop a computer-aided diagnosis system for neonatal cerebral disorders, some literatures have shown atlas-based methods for segmenting parenchymal region in MR images. Because neonatal cerebrum deforms quickly by natural growth, we desire an atlas growth model to improve the accuracy of segmenting parenchymal region. This paper proposes a method for generating fuzzy object growth model (FOGM), which is an extension of fuzzy object model (FOM). FOGM is composed of some growth index weighted FOMs. To define the growth index, this paper introduces two methods. The first method calculates the growth index from revised age. Because the growth index will be different from person to person even through the same age, the second method estimates the growth index from cerebral shape using Manifold learning. To evaluate the proposed methods, we segment the parenchymal region of 16 subjects (revised age; 0-2 years old) using the synthesized FOGM. The results showed that FOGM was superior to FOM, and the Manifold learning based method gave the best accuracy. And, the growth index estimated with Manifold learning was significantly correlated with both of revised age and cerebral volume (p<;0.001).