Dongqing Chen

3D vertebrae segmentation using graph cuts with shape prior constraints

Osteoporosis is a bone disease characterized by a reduction in bone mass, resulting in an increased risk of fractures. To diagnose the osteoporosis accurately, bone mineral density (BMD) measurements and fracture analysis (FA) of the Vertebral bodies (VBs) are required. In this paper, we propose a robust and 3D shape based method to segment VBs in clinical computed tomography (CT) images in order to make BMD measurements and FA accurately. In this experiment, image appearance and shape information of VBs are used. In the training step, 3D shape information is obtained from a set of data sets. Then, we estimate the shape variations using a distance probabilistic model which approximates the marginal densities of the VB and background in the variability region. In the segmentation step, the Matched filter is used to detect the VB region automatically. We align the detected volume with 3D shape prior in order to be used in distance probabilistic model. Then, the graph cuts method which integrates the linear combination of Gaussians (LCG), Markov Gibbs Random Field (MGRF), and distance probabilistic model obtained from 3D shape prior is used.

Accurate and fast 3D colon segmentation in CT colonography

This paper introduces an adaptive level set method for 3D segmentation of colon tissue in CT colonography filled with air and opacified fluid. First, most of the opacified liquid is removed by a threshold value. The closed contours are propagated toward the desired 3D region boundaries through the iterative evolution of the adaptive level sets function. The proposed method has been tested on 22 real CT colonography datasets with various pathologies, and the segmentation accuracy has achieved 98.40%.

Validation of Finite Element Models of Liver Tissue Using Micro-CT

In this work, the authors aim at validating some soft tissue deformation models using high-resolution micro-computed tomography (micro-CT) images. The imaging technique plays a key role in detecting the tissue deformation details in the contact region between the tissue and the surgical tool (probe) for small force loads and provides good capabilities of creating accurate 3D models of soft tissues. Surgical simulations rely on accurate representation of the mechanical response of soft tissues subjected to surgical manipulations. Several finite-element models have been suggested to characterize soft tissues. However, validating these models for specific tissues still remain a challenge. In this study, ex vivo lamb liver tissue is chosen to validate the linear elastic model (LEM), the linear viscoelastic model (LVEM), and the neo-Hooke hyperelastic model (NHM). We find that the LEM is more applicable to lamb liver than the LVEM for smaller force loads (<20 g) and that the NHM is closer to reality than the LVEM for the range of force loads from 5 to 40 g.

Shape from shading for hybrid surfaces as applied to tooth reconstruction

Accurate 3-D modeling of the human teeth helps patients avoid the discomfort of the mold process, and improves the data accuracy for oral surgeons, orthodontists and dental care personnel. Since the surface of the human tooth is almost textureless, Shape from Shading (SFS) has been successfully adopted in solving this problem. This paper evaluates 3-D tooth reconstruction using three SFS models. Based on quantitative error analysis and curvature analysis using Robust Point Matching (RPM) and visualization results, the assumption of a perspective camera projection and an Oren-Nayar reflectance model has been proved to be the most ideal for extracting crown information from an image of a human tooth. Three reconstructed teeth have been shown to demonstrate the performance of this model.

Geometric Features Based Framework for Colonic Polyp Detection using a New Color Coding Scheme

Curvature-based geometric features have been proven to be important for colonic polyp detection. In this paper, we present an automatic detection framework and color coding scheme to highlight the detected polyps. The key idea is to place the detected polyps at the same locations in a newly created polygonal dataset with the same topology and geometry properties as the triangulated mesh surface of real colon dataset, and assign different colors to the two separated datasets to highlight the polyps. Finally, we validate the proposed framework by computer simulated and real colon datasets. For fifteen synthetic polyps with different shapes and different sizes, the sensitivity is 100%, and false positive is 0. For four real colon datasets, the proposed algorithm has achieved the sensitivity of 75%.

An improved 2d colonic polyp segmentation framework based on gradient vector flow deformable model

Computed Tomography Colonography has been proved to be a valid technique for detecting and screening colorectal cancers. In this paper, we present a framework for colonic polyp detection and segmentation. Firstly, we propose to use four different geometric features for colonic polyp detection, which include shape index, curvedness, sphericity ratio and the absolute value of inner product of maximum principal curvature and gradient vector flow. Then, we use the bias-corrected fuzzy c-mean algorithm and gradient vector flow based deformable model for colonic polyp segmentation. Finally, we measure the overlap between the manual segmentation and the algorithm segmentation to test the accuracy of our frame work. The quantitative experiment results have shown that the average overlap is 85.17%±3.67%.

3D Vertebrae Segmentation in CT Images with Random Noises

Exposure levels (X-ray tube amperage and peak kilovoltage) are associated with various noise levels and radiation dose. When higher exposure levels are applied, the images have higher signal to noise ratio (SNR) in the CT images. However, the patient receives higher radiation dose in this case. In this paper, we use our robust 3D framework to segment vertebral bodies (VBs) in clinical computed tomography (CT) images with different noise levels. The Matched filter is employed to detect the VB region automatically. In the graph cuts method, a VB (object) and surrounding organs (background) are represented using a gray level distribution models which are approximated by a linear combination of Gaussians (LCG). Initial segmentation based on the LCG models is then iteratively refined by using Markov Gibbs random field(MGRF) with analytically estimated potentials. Experiments on the data sets show that the proposed segmentation approach is more accurate and robust than other known alternatives.

Curvature Flow Based 3D Surface Evolution Model for Polyp Detection and Visualization in CT Colonography

Computerized Tomography (CT) colonography is an emerging noninvasive technique for screening and diagnosing colon cancers. Since colonic polyps grow outward from the colon wall, they are modeled as protrusion shapes. In this chapter, we propose a novel anisotropic 3D surface evolution model for detecting protrusion shape based colonic polyp on the curved surface. The important feature of the proposed model is that it can detect protrusions with both convex and concave shapes. Protrusion shapes are defined as the extension beyond the usual limits or above a plane surface. Based on Gaussian and mean curvature flows, the approach works by locally deforming the convex or concave surface until the second principal curvature goes to zero. The diffusion directions are changed to prevent convex surfaces from converting into concave shapes, and vice versa. The deformation field quantitatively measures the amount of protrudeness. We also designed a new color coding scheme for better visualization of the detected polyps. The proposed method has been evaluated by using synthetic phantoms and real colon datasets.

Shape modeling of the corpus callosum

A novel approach for shape modeling of the corpus callosum (cc) is introduced where the contours of the cc are extracted by image/volume segmentation, and a Bezier curve is used to connect the vertices of the sampled contours, generating a parametric polynomial representation. These polynomials are shown to maintain the characteristics of the original cc, thus are suitable for classification of populations. The Bernstein polynomials are used in fitting the Bezier curves. The coefficients of the Bernstein polynomials are shown to capture the geometric features of the cc, and are able to describe deformations. We use these coefficients, in conjunction with the Fourier Descriptors and other features, to discriminate between autistic and normal brains. The approach is tested on T1-weighted MRI scans of 16 normal and 22 autistic subjects and shows its ability to provide perfect classification, suggesting that the approach is worth investigating on a larger population with the hope of providing early identification and intervention of autism using neuroimaging.

A 3D human teeth database construction based on a point-based shape registration

We propose a new system for building a 3D database for human teeth. Real extracted human teeth are scanned using a cone-beam CT scanner to build. The teeth models are segmented from the resulting images by means of level sets. The database includes 3D models of these teeth after minimizing global differences between the models. An affine transformation function is used to model the differences between the models. The transformation parameters are estimated by minimizing a least squares energy. A total of 280 teeth 3-D models have been created to build the real database and demonstrate the performance of the proposed framework.