Microaneurysms (MAs) are the first manifestations of the diabetic retinopathy (DR) as well as an indicator for its progression. Their automatic detection plays a key role for both mass screening and monitoring and is therefore in the core of any system for computer-assisted diagnosis of DR. The algorithm basically comprises the following stages: candidate detection aiming at extracting the patterns possibly corresponding to MAs based on mathematical morphological black top hat, feature extraction to characterize these candidates, and classification based on support vector machine (SVM), to validate MAs. Feature vector and kernel function of SVM selection is very important to the algorithm. We use the receiver operating characteristic (ROC) curve to evaluate the distinguishing performance of different feature vectors and different kernel functions of SVM. The ROC analysis indicates the quadratic polynomial SVM with a combination of features as the input shows the best discriminating performance.
Brain model has found wide applications in areas including surgical-path planning, image-guided surgery systems, and virtual medical environments. In comparison with the modeling of normal brain anatomy, the modeling of anatomical abnormalities appears to be rather weak. Particularly, there are considerable differences between abnormal brain images and normal brain images, due to the growth of brain tumor. In order to find the correspondence between abnormal brain images and normal ones, it is necessary to make an estimation or simulation of the brain deformation.
In this paper, a deformable model of brain tissue with both geometric and physical nonlinear properties based on finite element method is presented. It is assumed that the brain tissue are nonlinearly elastic solids obeying the equations of an incompressible nonlinearly elastics neo-Hookean model. we incorporate the physical inhomogeneous of brain tissue into our FEM model. The non-linearity of the model needs to solve the deformation of the model using an iteration method. The Updated Lagrange for iteration is used. To assure the convergence of iteration, we adopt the fixed arc length method.
This model has advantages over those linear models in its more real tissue properties and its capability of simulating more serious brain deformation.
The inclusion of second order displacement items into the balance and geometry functions allows for the estimation of more serious brain deformation. We referenced the model presented by Stelios K so as to ascertain the initial position of tumor as well as our tumor model definition. Furthermore, we expend it from 2-D to 3-D and simplify the calculation process.
In this paper we describe the pre-processing works of Virtual Chinese Human (VCH) dataset. And we developed a new hybrid volume render method which uses parallel computer system to implement high resolution visualization of the large dataset of VCH. Some research prospects of VCH dataset are also discussed.
Proc. SPIE. 4322, Medical Imaging 2001: Image Processing
KEYWORDS: Signal to noise ratio, Optical filters, Signal attenuation, Magnetic resonance imaging, Digital filtering, Medical imaging, Image filtering, Electronic filtering, Algorithm development, 3D image processing
In this paper an adaptive template filtering method is described which can be used to increase the signal-to-noise ratio(SNR) and keep the important edge information of medical images. To date various filtering approaches are reported, most of them enhance SNR in different levels with loss of some useful information. We try to develop a robust algorithm. Unlike conventional filtering, where the template shape and coefficients are fixed, multiple templates are defined in the proposed algorithm. For each pixel, an optimal template is selected automatically depending on its neighbor pixels. Simulation and MRI image tests, both 2D and 3D, show that the new adaptive template filter provides higher SNR and sharper edges. Our method improved existing adaptive template filtering technique, corrected scale factor of threshold adjustment, extended it to 3D algorithm.