Zhaohua Ding

Minimization of Region-Scalable Fitting Energy for Image Segmentation

Intensity inhomogeneities often occur in real-world images and may cause considerable difficulties in image segmentation. In order to overcome the difficulties caused by intensity inhomogeneities, we propose a region-based active contour model that draws upon intensity information in local regions at a controllable scale. A data fitting energy is defined in terms of a contour and two fitting functions that locally approximate the image intensities on the two sides of the contour. This energy is then incorporated into a variational level set formulation with a level set regularization term, from which a curve evolution equation is derived for energy minimization. Due to a kernel function in the data fitting term, intensity information in local regions is extracted to guide the motion of the contour, which thereby enables our model to cope with intensity inhomogeneity. In addition, the regularity of the level set function is intrinsically preserved by the level set regularization term to ensure accurate computation and avoids expensive reinitialization of the evolving level set function. Experimental results for synthetic and real images show desirable performances of our method.

A Level Set Method for Image Segmentation in the Presence of Intensity Inhomogeneities With Application to MRI

Intensity inhomogeneity often occurs in real-world images, which presents a considerable challenge in image segmentation. The most widely used image segmentation algorithms are region-based and typically rely on the homogeneity of the image intensities in the regions of interest, which often fail to provide accurate segmentation results due to the intensity inhomogeneity. This paper proposes a novel region-based method for image segmentation, which is able to deal with intensity inhomogeneities in the segmentation. First, based on the model of images with intensity inhomogeneities, we derive a local intensity clustering property of the image intensities, and define a local clustering criterion function for the image intensities in a neighborhood of each point. This local clustering criterion function is then integrated with respect to the neighborhood center to give a global criterion of image segmentation. In a level set formulation, this criterion defines an energy in terms of the level set functions that represent a partition of the image domain and a bias field that accounts for the intensity inhomogeneity of the image. Therefore, by minimizing this energy, our method is able to simultaneously segment the image and estimate the bias field, and the estimated bias field can be used for intensity inhomogeneity correction (or bias correction). Our method has been validated on synthetic images and real images of various modalities, with desirable performance in the presence of intensity inhomogeneities. Experiments show that our method is more robust to initialization, faster and more accurate than the well-known piecewise smooth model. As an application, our method has been used for segmentation and bias correction of magnetic resonance (MR) images with promising results.

Implicit Active Contours Driven by Local Binary Fitting Energy

Local image information is crucial for accurate segmentation of images with intensity inhomogeneity. However, image information in local region is not embedded in popular region-based active contour models, such as the piecewise constant models. In this paper, we propose a region-based active contour model that is able to utilize image information in local regions. The major contribution of this paper is the introduction of a local binary fitting energy with a kernel function, which enables the extraction of accurate local image information. Therefore, our model can be used to segment images with intensity inhomogeneity, which overcomes the limitation of piecewise constant models. Comparisons with other major region-based models, such as the piece-wise smooth model, show the advantages of our method in terms of computational efficiency and accuracy. In addition, the proposed method has promising application to image denoising.

Validation of diffusion tensor MRI-based muscle fiber tracking.

Diffusion‐tensor (DT) MRI fiber tracking may potentially be used for in vivo structural analysis. The purpose of this study was to assess quantitatively the ability of a DT‐MRI fiber‐tracking algorithm to measure the fiber orientation (pennation) in skeletal muscle in vivo. In five adult Sprague‐Dawley rats, the pennation angle (θ) was measured in the rat lateral gastrocnemius with DT‐MRI (θDT‐MRI) and by direct anatomical inspection (DAI) (θDAI). The mean θDT‐MRI was not significantly different from the mean θDAI. In addition, the two methods were highly correlated (r = 0.89) and the regression of θDT‐MRI on θDAI resulted in a slope not significantly different from 1 and an intercept not significantly different from zero. These data indicate that DT‐MRI‐based fiber tracking as implemented here is a valid tool for in vivo structural analysis of small‐animal skeletal muscle. Magn Reson Med 48:97–104, 2002.

Classification and quantification of neuronal fiber pathways using diffusion tensor MRI

Quantitative characterization of neuronal fiber pathways in vivo is of significant neurological and clinical interest. Using the capability of MR diffusion tensor imaging to determine the local orientations of neuronal fibers, novel algorithms were developed to bundle neuronal fiber pathways reconstructed in vivo with diffusion tensor images and to quantify various physical and geometric properties of fiber bundles. The reliability of the algorithms was examined with reproducibility tests. Illustrative results show that consistent physical and geometric measurements of novel properties of neuronal tissue can be obtained, which offer considerable potential for the quantitative study of fiber pathways in vivo. Magn Reson Med 49:716–721, 2003.

Effects of cardiac motion on right coronary artery hemodynamics.

AbstractThe purpose of this work was to investigate the effects of physiologically realistic cardiac-induced motion on hemodynamics in human right coronary arteries. The blood flow patterns were numerically simulated in a modeled right coronary artery (RCA) having a uniform circular cross section of 2.48 mm diam. Arterial motion was specified based on biplane cineangiograms, and incorporated physiologically realistic bending and torsion. Simulations were carried out with steady and pulsatile inflow conditions (mean ReD=233, α =1.82) in both fixed and moving RCA models, to evaluate the relative importance of RCA motion, flow pulsation, and the interaction between motion and flow pulsation. RCA motion with a steady inlet flow rate caused variations in wall shear stress (WSS) magnitude up to 150% of the inlet Poiseuille value. There was significant spatial variability in the magnitude of this motion-induced WSS variation. However, the time-averaged WSS distribution was similar to that predicted in a static model representing the time-averaged geometry. Furthermore, the effects of flow pulsatility dominated RCA motion-induced effects; specifically, there were only modest differences in the WSS history between simulations conducted in fixed and moving RCA models with pulsatile inflow. RCA motion has little effect on time-averaged WSS patterns. It has a larger effect on the temporal variation of WSS, but even this effect is overshadowed by the variations in WSS due to flow pulsation. The hemodynamic effects of RCA motion can, therefore, be ignored as a first approximation in modeling studies.

Early Adverse Events, HPA Activity and Rostral Anterior Cingulate Volume in MDD

Background Prior studies have independently reported associations between major depressive disorder (MDD), elevated cortisol concentrations, early adverse events and region-specific decreases in grey matter volume, but the relationships among these variables are unclear. In the present study, we sought to evaluate the relationships between grey matter volume, early adverse events and cortisol levels in MDD. Methods/Results Grey matter volume was compared between 19 controls and 19 individuals with MDD using voxel-based morphometry. A history of early adverse events was assessed using the Childhood Trauma Questionnaire. Subjects also provided salivary cortisol samples. Depressed patients showed decreased grey matter volume in the rostral ACC as compared to controls. Rostral ACC volume was inversely correlated with both cortisol and early adverse events. Conclusions These findings suggest a key relationship between ACC morphology, a history of early adverse events and circulating cortisol in the pathophysiology of MDD.

A Variational Level Set Approach to Segmentation and Bias Correction of Images with Intensity Inhomogeneity

This paper presents a variational level set approach to joint segmentation and bias correction of images with intensity inhomogeneity. Our method is based on an observation that intensities in a relatively small local region are separable, despite of the inseparability of the intensities in the whole image caused by the intensity inhomogeneity. We first define a weighted K-means clustering objective function for image intensities in a neighborhood around each point, with the cluster centers having a multiplicative factor that estimates the bias within the neighborhood. The objective function is then integrated over the entire domain and incorporated into a variational level set formulation. The energy minimization is performed via a level set evolution process. Our method is able to estimate bias of quite general profiles. Moreover, it is robust to initialization, and therefore allows automatic applications. The proposed method has been used for images of various modalities with promising results.

Reduction of Noise in Diffusion Tensor Images Using Anisotropic Smoothing

To improve the accuracy of tissue structural and architectural characterization with diffusion tensor imaging, a novel smoothing technique is developed for reducing noise in diffusion tensor images. The technique extends the traditional anisotropic diffusion filtering method by allowing isotropic smoothing within homogeneous regions and anisotropic smoothing along structure boundaries. This is particularly useful for smoothing diffusion tensor images in which direction information contained in the tensor needs to be restored following noise corruption and preserved around tissue boundaries. The effectiveness of this technique is quantitatively studied with experiments on simulated and human in vivo diffusion tensor data. Illustrative results demonstrate that the anisotropic smoothing technique developed can significantly reduce the impact of noise on the direction as well as anisotropy measures of the diffusion tensor images. Magn Reson Med 53:485–490, 2005.

Using High-Resolution MR Imaging at 7T to Evaluate the Anatomy of the Midbrain Dopaminergic System

BACKGROUND AND PURPOSE: Dysfunction of DA neurotransmission from the SN and VTA has been implicated in neuropsychiatric diseases, including Parkinson disease and schizophrenia. Unfortunately, these midbrain DA structures are difficult to define on clinical MR imaging. To more precisely evaluate the anatomic architecture of the DA midbrain, we scanned healthy participants with a 7T MR imaging system. Here we contrast the performance of high-resolution T2- and T2*-weighted GRASE and FFE MR imaging scans at 7T. MATERIALS AND METHODS: Ten healthy participants were scanned by using GRASE and FFE sequences. CNRs were calculated among the SN, VTA, and RN, and their volumes were estimated by using a segmentation algorithm. RESULTS: Both GRASE and FFE scans revealed visible contrast between midbrain DA regions. The GRASE scan showed higher CNRs compared with the FFE scan. The T2* contrast of the FFE scan further delineated substructures and microvasculature within the midbrain SN and RN. Segmentation and volume estimation of the midbrain SN, RN, and VTA showed individual differences in the size and volume of these structures across participants. CONCLUSIONS: Both GRASE and FFE provide sufficient CNR to evaluate the anatomy of the midbrain DA system. The FFE in particular reveals vascular details and substructure information within the midbrain regions that could be useful for examining structural changes in midbrain pathologies.