Ye Duan

Shape Reconstruction from 3D and 2D Data Using PDE-Based Deformable Surfaces

In this paper, we propose a new PDE-based methodology for deformable surfaces that is capable of automatically evolving its shape to capture the geometric boundary of the data and simultaneously discover its underlying topological structure. Our model can handle multiple types of data (such as volumetric data, 3D point clouds and 2D image data), using a common mathematical framework. The deformation behavior of the model is governed by partial differential equations (e.g. the weighted minimal surface flow). Unlike the level-set approach, our model always has an explicit representation of geometry and topology. The regularity of the model and the stability of the numerical integration process are ensured by a powerful Laplacian tangential smoothing operator. By allowing local adaptive refinement of the mesh, the model can accurately represent sharp features. We have applied our model for shape reconstruction from volumetric data, unorganized 3D point clouds and multiple view images. The versatility and robustness of our model allow its application to the challenging problem of multiple view reconstruction. Our approach is unique in its combination of simultaneous use of a high number of arbitrary camera views with an explicit mesh that is intuitive and easy-to-interact-with. Our model-based approach automatically selects the best views for reconstruction, allows for visibility checking and progressive refinement of the model as more images become available. The results of our extensive experiments on synthetic and real data demonstrate robustness, high reconstruction accuracy and visual quality.

Facial phenotypes in subgroups of prepubertal boys with autism spectrum disorders are correlated with clinical phenotypes

BackgroundThe brain develops in concert and in coordination with the developing facial tissues, with each influencing the development of the other and sharing genetic signaling pathways. Autism spectrum disorders (ASDs) result from alterations in the embryological brain, suggesting that the development of the faces of children with ASD may result in subtle facial differences compared to typically developing children. In this study, we tested two hypotheses. First, we asked whether children with ASD display a subtle but distinct facial phenotype compared to typically developing children. Second, we sought to determine whether there are subgroups of facial phenotypes within the population of children with ASD that denote biologically discrete subgroups.MethodsThe 3dMD cranial System was used to acquire three-dimensional stereophotogrammetric images for our study sample of 8- to 12-year-old boys diagnosed with essential ASD (n = 65) and typically developing boys (n = 41) following approved Institutional Review Board protocols. Three-dimensional coordinates were recorded for 17 facial anthropometric landmarks using the 3dMD Patient software. Statistical comparisons of facial phenotypes were completed using Euclidean Distance Matrix Analysis and Principal Coordinates Analysis. Data representing clinical and behavioral traits were statistically compared among groups by using χ2 tests, Fishers exact tests, Kolmogorov-Smirnov tests and Students t-tests where appropriate.ResultsFirst, we found that there are significant differences in facial morphology in boys with ASD compared to typically developing boys. Second, we also found two subgroups of boys with ASD with facial morphology that differed from the majority of the boys with ASD and the typically developing boys. Furthermore, membership in each of these distinct subgroups was correlated with particular clinical and behavioral traits.ConclusionsBoys with ASD display a facial phenotype distinct from that of typically developing boys, which may reflect alterations in the prenatal development of the brain. Subgroups of boys with ASD defined by distinct facial morphologies correlated with clinical and behavioral traits, suggesting potentially different etiologies and genetic differences compared to the larger group of boys with ASD. Further investigations into genes involved in neurodevelopment and craniofacial development of these subgroups will help to elucidate the causes and significance of these subtle facial differences.

Thalamus segmentation from diffusion tensor magnetic resonance imaging

In this paper, we propose a semi-automatic thalamus and thalamus nuclei segmentation algorithm from diffusion tensor magnetic resonance imaging (DT-MRI) based on the mean-shift algorithm. Comparing with existing thalamus segmentation algorithms which are mainly based on K-means algorithm, our mean-shift based algorithm is more flexible and adaptive. It does not assume a Gaussian distribution or a fixed number of clusters. Furthermore, the single parameter in the mean-shift based algorithm supports hierarchical clustering naturally

Automated Tongue Feature Extraction for ZHENG Classification in Traditional Chinese Medicine

ZHENG, Traditional Chinese Medicine syndrome, is an integral and essential part of Traditional Chinese Medicine theory. It defines the theoretical abstraction of the symptom profiles of individual patients and thus, used as a guideline in disease classification in Chinese medicine. For example, patients suffering from gastritis may be classified as Cold or Hot ZHENG, whereas patients with different diseases may be classified under the same ZHENG. Tongue appearance is a valuable diagnostic tool for determining ZHENG in patients. In this paper, we explore new modalities for the clinical characterization of ZHENG using various supervised machine learning algorithms. We propose a novel-color-space-based feature set, which can be extracted from tongue images of clinical patients to build an automated ZHENG classification system. Given that Chinese medical practitioners usually observe the tongue color and coating to determine a ZHENG type and to diagnose different stomach disorders including gastritis, we propose using machine-learning techniques to establish the relationship between the tongue image features and ZHENG by learning through examples. The experimental results obtained over a set of 263 gastritis patients, most of whom suffering Cold Zheng or Hot ZHENG, and a control group of 48 healthy volunteers demonstrate an excellent performance of our proposed system.

A subdivision-based deformable model for surface reconstruction of unknown topology

This paper presents a surface reconstruction algorithm that can recover correct shape geometry as well as its unknown topology from both volumetric images and unorganized point clouds. The algorithm starts from a simple seed model (of genus zero) that can be arbitrarily initiated within any datasets. The deformable behavior of the model is governed by a locally defined objective function associated with each vertex of the model. Through the numerical computation of function optimization, the algorithm can adaptively subdivide the model geometry, automatically detect self-collision of the model, properly modify its topology (because of the occurrence of self-collision), continuously evolve the model towards the object boundary, and reduce fitting error and improve fitting quality via global refinement. Commonly used mesh optimization techniques are employed throughout the geometric deformation and topological variation to ensure the model both locally smooth and globally well defined. Our experiments have demonstrated that the new modeling algorithm is valuable for iso-surface extraction in visualization, shape recovery and segmentation in medical imaging, and surface reconstruction in reverse engineering.

An automatic tongue detection and segmentation framework for computer–aided tongue image analysis

Traditional Chinese Medicine (TCM) has a long history and has been recognized as a popular alternative medicine in western countries. Tongue diagnosis is a significant procedure in computer-aided TCM, where tongue image analysis plays a dominant role. In this paper, we proposed a fully automatic tongue detection and tongue segmentation framework, which is an essential step in computer-aided tongue image analysis. Comparing with other existing methods, our method is fully automatic without any need of adjusting parameters for different images and do not need any initialization.

A Fast, Semi-automatic Brain Structure Segmentation Algorithm for Magnetic Resonance Imaging

Medical image segmentation has become an essential technique in clinical and research-oriented applications. Because manual segmentation methods are tedious, and fully automatic segmentation lacks the flexibility of human intervention or correction, semi-automatic methods have become the preferred type of medical image segmentation. We present a hybrid, semi-automatic segmentation method in 3D that integrates both region-based and boundary-based procedures. Our method differs from previous hybrid methods in that we perform region-based and boundary-based approaches separately, which allows for more efficient segmentation. A region-based technique is used to generate an initial seed contour that roughly represents the boundary of a target brain structure, alleviating the local minima problem in the subsequent model deformation phase. The contour is deformed under a unique force equation independent of image edges. Experiments on MRI data show that this method can achieve high accuracy and efficiency primarily due to the unique seed initialization technique.

A Context-Sensitive Active Contour for 2D Corpus Callosum Segmentation

We propose a new context-sensitive active contour for 2D corpus callosum segmentation. After a seed contour consisting of interconnected parts is being initialized by the user, each part will start to deform according to its own motion law derived from high-level prior knowledge, and is constantly aware of its own orientation and destination during the deformation process. Experimental results demonstrate the accuracy and robustness of our algorithm.

Interactive shape modeling using Lagrangian surface flow

In this paper, we propose a new shape-modeling paradigm based on the concept of Lagrangian surface flow. Given an input polygonal model, the user interactively defines a distance field around regions of interest; the locally or globally affected regions will then automatically deform according to the user-defined distance field. During the deformation process, the model can always maintain its regularity and can properly modify its topology by topology merging when collisions between two different parts of the model occur. Comparing with level-set based methods, our algorithm allows the user to work directly on existing polygonal models without any intermediate model conversion. Besides closed polygonal models, our algorithm also works for mesh models with open boundaries. Within our framework, we developed a number of shape-modeling operators including blending, cutting, drilling, free-hand sketching, and mesh warping. We applied our algorithm to a variety of examples that demonstrate the usefulness and efficacy of the new technique in interactive shape design and surface deformation.

Quantitative Phenotyping of Duchenne Muscular Dystrophy Dogs by Comprehensive Gait Analysis and Overnight Activity Monitoring

The dystrophin-deficient dog is excellent large animal model for testing novel therapeutic modalities for Duchenne muscular dystrophy (DMD). Despite well-documented descriptions of dystrophic symptoms in these dogs, very few quantitative studies have been performed. Here, we developed a comprehensive set of non-invasive assays to quantify dog gait (stride length and speed), joint angle and limb mobility (for both forelimb and hind limb), and spontaneous activity at night. To validate these assays, we examined three 8-m-old mix-breed dystrophic dogs. We also included three age-matched siblings as the normal control. High-resolution video recorders were used to digitize dog walking and spontaneous movement at night. Stride speed and length were significantly decreased in affected dogs. The mobility of the limb segments (forearm, front foot, lower thigh, rear foot) and the carpus and hock joints was significantly reduced in dystrophic dogs. There was also a significant reduction of the movement in affected dogs during overnight monitoring. In summary, we have established a comprehensive set of outcome measures for clinical phenotyping of DMD dogs. These non-invasive end points would be valuable in monitoring disease progression and therapeutic efficacy in translational studies in the DMD dog model.