Yongxin Zhou

CT data has been widely used in the finite element modeling of bone. It can provide useful information on the geometrical topology and material properties of bone. Based on CT data, the assignment of bone material properties to finite element meshes is a fundamental step in the model generation. Most work done in this area has adopted isotropic assignment strategy due to its simplicity. However, bone material has been recognized as an orthotropic material. This work is aimed to investigate the effects of orthotropic material property assignment on femoral finite element model by comparing with isotropic material property assignment on the same model. There were 72 finite element models obtained from the frozen CT male dataset of visible human project. Based on the analysis results of the maximum equivalent Von Mises stress and the maximum nodal displacement, three parameters were defined to achieve this comparison. The results have shown that the differences between the two material property assignments are small under two loading conditions (double-leg standing and single-leg standing) investigated in this work.

Organ segmentation is an important step in various medical image applications. In this paper, a presegmented atlas is incorporated into the fuzzy connectedness (FC) framework for automatic segmentation of abdominal organs. First, the atlas is registered onto the subject to provide an initial segmentation. Then, a novel method is applied to estimate the necessary FC parameters such as organ intensity features, seeds, and optimal FC threshold automatically and subject adaptively. In order to overcome the intensity overlapping between the neighboring organs, a shape modification approach based on Euclidean distance and watershed segmentation is used. This atlas-based segmentation method has been tested on some abdominal CT and MRI images from Chinese patients. Experimental results indicate the validity of this segmentation method for various image modalities.

A framework that combines atlas registration, fuzzy connectedness (FC) segmentation, and parametric bias field correction (PABIC) is proposed for the automatic segmentation of brain magnetic resonance imaging (MRI). First, the atlas is registered onto the MRI to initialize the following FC segmentation. Original techniques are proposed to estimate necessary initial parameters of FC segmentation. Further, the result of the FC segmentation is utilized to initialize a following PABIC algorithm. Finally, we re-apply the FC technique on the PABIC corrected MRI to get the final segmentation. Thus, we avoid expert human intervention and provide a fully automatic method for brain MRI segmentation. Experiments on both simulated and real MRI images demonstrate the validity of the method, as well as the limitation of the method. Being a fully automatic method, it is expected to find wide applications, such as three-dimensional visualization, radiation therapy planning, and medical database construction.

O. In addition, DNA induced photoluminescence enhancement was observed for the monomer. As a result, this AIEgen-based 2D SOF could feature not only as a cell visualizer but also as a tracker for the nucleus in biological imaging due to the dynamic assembly process.