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Abstract Background This paper presents a three-dimensional (3D) method for segmenting corpus callosum in normal subjects and brain cancer patients with glioblastoma. Methods Nineteen patients with histologically confirmed treatment naïve glioblastoma and eleven normal control subjects underwent DTI on a 3T scanner. Based on the information inherent in diffusion tensors, a similarity measure was proposed and used in the proposed algorithm. In this algorithm, diffusion pattern of corpus callosum was used as prior information. Subsequently, corpus callosum was automatically divided into Witelson subdivisions. We simulated the potential rotation of corpus callosum under tumor pressure and studied the reproducibility of the proposed segmentation method in such cases. Results Dice coefficients, estimated to compare automatic and manual segmentation results for Witelson subdivisions, ranged from 94% to 98% for control subjects and from 81% to 95% for tumor patients, illustrating closeness of automatic and manual segmentations. Studying the effect of corpus callosum rotation by different Euler angles showed that although segmentation results were more sensitive to azimuth and elevation than skew, rotations caused by brain tumors do not have major effects on the segmentation results. Conclusions The proposed method and similarity measure segment corpus callosum by propagating a hyper-surface inside the structure (resulting in high sensitivity), without penetrating into neighboring fiber bundles (resulting in high specificity).

Abstract Background Pre-operative imaging devices generate high-resolution images but intra-operative imaging devices generate low-resolution images. To use high-resolution pre-operative images during surgery, they must be deformed to reflect intra-operative geometry of brain. Methods We employ biomechanical models, guided by low resolution intra-operative images, to determine location of normal and abnormal regions of brain after craniotomy. We also employ finite element methods to discretize and solve the related differential equations. In the process, pre- and intra-operative images are utilized and corresponding points are determined and used to optimize parameters of the models. This paper develops a nonlinear model and compares it with linear models while our previous work developed and compared linear models (mechanical and elastic). Results Nonlinear model is evaluated and compared with linear models using simulated and real data. Partial validation using intra-operative images indicates that the proposed models reduce the localization error caused by brain deformation after craniotomy. Conclusions The proposed nonlinear model generates more accurate results than the linear models. When guided by limited intra-operative surface data, it predicts deformation of entire brain. Its execution time is however considerably more than those of linear models.

This work addresses the increasing demand for a sensitive and user-friendly iris based authentication system. We aim at reducing False Rejection Rate (FRR). The primary source of high FRR is the presence of degradation factors in iris texture. To reduce FRR, we propose a feature extraction method robust against such adverse factors. Founded on local and global variations of the texture, this method is designed to particularly cope with blurred and unfocused iris images. Global variations extract a general presentation of texture, while local yet soft variations encode texture details that are minimally reliant on the image quality. Discrete Cosine Transform and wavelet decomposition are used to capture the local and global variations. In the matching phase, a support vector machine fuses similarity values obtained from global and local features. The verification performance of the proposed method is examined and compared on CASIA Ver.1 and UBIRIS databases. Efficiency of the method contending with degraded images of the UBIRIS is corroborated by experimental results where a significant decrease in FRR is observed in comparison with other algorithms. The experiments on CASIA show that despite neglecting detailed texture information, our method still provides results comparable to those of recent methods.

Abstract Background Despite enormous progress in gene therapy for breast cancer, an optimal systemic vehicle for delivering gene products to the target tissue is still lacking. The purpose of this study was to determine whether AC133+ progenitor cells (APC) can be used as both gene delivery vehicles and cellular probes for magnetic resonance imaging (MRI). In this study, we used superparamagentic iron oxide (SPIO)-labeled APCs to carry the human sodium iodide symporter (hNIS) gene to the sites of implanted breast cancer in mouse model. In vivo real time tracking of these cells was performed by MRI and expression of hNIS was determined by Tc-99m pertechnetate (Tc-99m) scan. Results Three million human breast cancer (MDA-MB-231) cells were subcutaneously implanted in the right flank of nude mice. APCs, isolated from fresh human cord blood, were genetically transformed to carry the hNIS gene using adenoviral vectors and magnetically labeled with ferumoxides-protamine sulfate (FePro) complexes. Magnetically labeled genetically transformed cells were administered intravenously in tumor bearing mice when tumors reached 0.5 cm in the largest dimension. MRI and single photon emission computed tomography (SPECT) images were acquired 3 and 7 days after cell injection, with a 7 Tesla animal MRI system and a custom built micro-SPECT using Tc-99m, respectively. Expression of hNIS in accumulated cells was determined by staining with anti-hNIS antibody. APCs were efficiently labeled with ferumoxide-protamine sulfate (FePro) complexes and transduced with hNIS gene. Our study showed not only the accumulation of intravenously administered genetically transformed, magnetically labeled APCs in the implanted breast cancer, but also the expression of hNIS gene at the tumor site. Tc-99m activity ratio (tumor/non-tumor) was significantly different between animals that received non-transduced and transduced cells (P < 0.001). Conclusion This study indicates that genetically transformed, magnetically labeled APCs can be used both as delivery vehicles and cellular probes for detecting in vivo migration and homing of cells. Furthermore, they can potentially be used as a gene carrier system for the treatment of tumor or other diseases.