Jianhua Xuan

Segmentation of magnetic resonance brain image: integrating region growing and edge detection

The authors present a method that combines region growing and edge detection for magnetic resonance (MR) brain image segmentation. Starting with a simple region growing algorithm which produces an over segmented image, the authors apply a sophisticated region merging method which is capable of handling complex image structures. Edge information is then integrated to verify and, where necessary, to correct region boundaries. The results show that this method is reliable and efficient for MR brain image segmentation.

Magnetic resonance image analysis by information theoretic criteria and stochastic site models

Quantitative analysis of magnetic resonance (MR) images is a powerful tool for image-guided diagnosis, monitoring, and intervention. The major tasks involve tissue quantification and image segmentation where both the pixel and context images are considered. To extract clinically useful information from images that might be lacking in prior knowledge, the authors introduce an unsupervised tissue characterization algorithm that is both statistically principled and patient specific. The method uses adaptive standard finite normal mixture and inhomogeneous Markov random field models, whose parameters are estimated using expectation-maximization and relaxation labeling algorithms under information theoretic criteria. The authors demonstrate the successful applications of the approach with synthetic data sets and then with real MR brain images.

Surface reconstruction and visualization of the surgical prostate model

An advanced image analysis and graphics software is developed to reconstruct and visualize previously images prostate specimens to define tumor volume and distribution and pathways of needle biopsies, thus allowing improved understanding of prostate cancer behavior and current diagnosis-staging methodology. In order to reconstruct an accurate surface model of the surgical prostate, contour interpolation and surface reconstruction are performed on extracted contours of the object of interest. Contour interpolation increases the sample rate in the stacking direction in order to reconstruct sufficiently accurate surfaces of the prostate and its internal anatomical structures. An elastic contour model is developed through computing a force field between adjacent slices to deform the start contour gradually to conform to the target contour. A new finite-element deformable surface-spine model is then developed to reconstruct the computerized prostate model from the interpolated contours. A deformable spine of the prostate model is determined from its contours, and all the surface patches are contracted to the spine through expansion/compression forces radiating form the spine while the spine itself is also confined to the surface. The surface refinement is governed by a second-order partial differential equation from Lagrangian mechanics, and the refining process is accomplished when the energy of this dynamic deformable surface-spine model reaches its minimum. Interactive visualization is achieved by using the state-of- the-art 3D graphics toolkit, OpenInventor, with graphical user interface to visualize the reconstructed 3D prostate model including all internal anatomical structures and their relationships. Finally, an image-guided prostate needle biopsy simulation is implemented to validate current biopsy strategies on tumor detection and tumor volume estimation to improve prostate needle biopsy techniques.

A deformable surface-spine model for 3-D surface registration

A finite-element deformable surface-spine model is developed in this paper to register two surfaces by recovering the nonlinear deformation with respect to each other. The deformable surface-spine model is a dynamic model governed by Lagrangian motion equations. A 9 degree-of-freedom (dof) finite-element surface element and a 4-dof spine element are developed to iteratively solve Lagrangian equations for computing the deformation between two surfaces. The method has been applied to registration of computerized surgical prostate models. Experimental results have demonstrated that the new registration method can successfully match complex-structured surfaces by recovering the nonlinear deformation.

Information geometry of maximum partial likelihood estimation for channel equalization

Information geometry of partial likelihood is constructed and is used to derive the em-algorithm for learning parameters of a conditional distribution model through information-theoretic projections. To construct the coordinates of the information geometry, an expectation maximization (EM) framework is described for the distribution learning problem using the Gaussian mixture probability model. It is shown that the information-geometric em-algorithm is equivalent to EM to establish its convergence. The algorithm is applied to channel equalization by distribution learning and its rapid convergence characteristics are demonstrated through simulation studies.

Automatic detection of foreign objects in computed radiography

This paper presents an effective two-step scheme for automatic object detection in computed radiography (CR) images. First, various structure elements of the morphological filters, designed by incorporating available morphological features of the objects of interest including their sizes and rough shape descriptions, are used to effectively distinguish the foreign object candidates from the complex background structures. Second, since the boundaries of the objects are the key features in reflecting object characteristics, active contour models are employed to accurately outline the morphological shapes of the suspicious foreign objects to further reduce the rate of false alarms. The actual detection scheme is accomplished by jointly using these two steps. The proposed methods are tested with a database of 50 hand–wrist computed radiographic images containing various types of foreign objects. Our experimental results demonstrate that the combined use of morphological filters and active contour models can provide an effective automatic detection of foreign objects in CR images achieving good sensitivity and specificity, and the accurate descriptions of the object morphological characteristics.

Predictive tree-structured vector quantization for medical image compression and its evaluation with computerized image analysis

We present a predictive learning tree-structured vector quantization technique for medical image compression. A multi-layer perceptron (MLP) based vector predictor is employed to remove first as well as higher order correlations that exist among neighboring pixels. We use a learning tree-structured vector quantization (LTSVQ) scheme, which is based on competitive learning (CL) algorithm, to encode the residual vector. LTSVQ algorithm is computationally very efficient, easy to implement and provides performance comparable to that of LBG (Linde, Buzo and Gray) algorithm. We use computerized image analysis (image segmentation) as well as mean square error (MSE) and signal-to-noise ratio (SNR) to evaluate the quality of the compressed images. We apply the neural network based predictive LTSVQ to mammographic and magnetic resonance (MR) images, and evaluate the quality of images with different compression ratios.