Tian Shen

Rutgers, The State University of New Jersey, New Brunswick, New Jersey, United States

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Publications (21)15.71 Total impact

  • [Show abstract] [Hide abstract]
    ABSTRACT: Computer-aided diagnosis of masses in mammograms is important to the prevention of breast cancer. Many approaches tackle this problem through content-based image retrieval (CBIR) techniques. However, most of them fall short of scalability in the retrieval stage, and their diagnostic accuracy is therefore restricted. To overcome this drawback, we propose a scalable method for retrieval and diagnosis of mam-mographic masses. Specifically, for a query mammographic region of interest (ROI), SIFT descriptors are extracted and searched in a vocabulary tree, which stores all the quantized descriptors of previously diagnosed mammographic ROIs. In addition, to fully exert the discriminative power of SIFT descriptors, contextual information in the vocabulary tree is employed to refine the weights of tree nodes. The retrieved ROIs are then used to determine whether the query ROI contains a mass. This method has excellent scalability due to the low spatial-temporal cost of vocabulary tree. Retrieval precision and diagnostic accuracy are evaluated on 5005 ROIs extracted from the digital database for screening mammography (DDSM), which demonstrate the efficacy of our approach.
    2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI 2014); 04/2014
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    ABSTRACT: In this paper, we propose a method to segment multiple rodent brain structures simultaneously. This method combines deformable models and hierarchical shape priors within one framework. The deformation module employs both gradient and appearance information to generate image forces to deform the shape. The shape prior module uses Principal Component Analysis to hierarchically model the multiple structures at both global and local levels. At the global level, the statistics of relative positions among different structures are modeled. At the local level, the shape statistics within each structure is learned from training samples. Our segmentation method adaptively employs both priors to constrain the intermediate deformation result. This prior constraint improves the robustness of the model and benefits the segmentation accuracy. Another merit of our prior module is that the size of the training data can be small, because the shape prior module models each structure individually and combines them using global statistics. This scheme can preserve shape details better than directly applying PCA on all structures. We use this method to segment rodent brain structures, such as the cerebellum, the left and right striatum, and the left and right hippocampus. The experiments show that our method works effectively and this hierarchical prior improves the segmentation performance.
    09/2011: pages 611-618;
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    ABSTRACT: In this paper, we introduce a novel method to solve shape alignment problems. We use gray-scale "images” to represent source shapes, and propose a novel two-component Gaussian Mixture (GM) distance map representation for target shapes. This asymmetric representation is a flexible image-based representation which is able to represent different kinds of shape data, including continuous contours, unstructured sparse point sets, edge maps, and even gray-scale gradient maps. Using this representation, a new energy function based on a novel two-component Gaussian Mixture distance model is proposed. The new energy function was empirically evaluated to be a more robust shape dissimilarity metric that can be computed efficiently. Such high efficiency is essential for global optimization methods. We adopt and modify one of them, the Particle Swarm Optimization (PSO), to effectively estimate the global optimum of the new energy function. Differently from the original PSO, several new strategies were employed to make the optimization more robust and prevent it from converging prematurely. The overall performance of the proposed framework as well as the properties of each algorithmic component were evaluated and compared with those of some state-of-the-art methods. Extensive experiments and comparison performed on generalized 2D and 3D shape data demonstrate the robustness and effectiveness of the method.
    IEEE Transactions on Pattern Analysis and Machine Intelligence 07/2011; · 4.80 Impact Factor
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    ABSTRACT: For the purpose of object boundary extraction, traditional shape-based deformable models rely on external image forces that come primarily from edge or image gradient information. Such reliance on local edge information makes the models prone to get stuck in local minima due to image noise and various other image artifacts. In this chapter, we review a 2D deformable model – Metamorphs, which integrates region texture constraints so as to achieve more robust segmentation. Compared with traditional shape-based models, Metamorphs segmentation result is less dependent on model initialization and not sensitive to noise and spurious edges inside the object of interest. Then, we review Active Volume Model (AVM), a similar and improved approach for 3D segmentation. The shape of this 3D model is considered as an elastic solid, with a simplex-mesh surface made of thousands of vertices. Deformations of the model are derived from a linear system that encodes external forces from the boundary of a Region of Interest (ROI), which is a binary mask representing the object region predicted by the current model. Efficient optimization and fast convergence of the model are achieved using the Finite Element Method (FEM). To further improve segmentation performance, a multiple-surface constraint is also employed to incorporate spatial constraints among multiple objects. It uses two surface distance-based functions to adaptively adjust the weights of contribution from the image-based region information and from spatial constraints among multiple interacting surfaces. Several applications are shown to demonstrate the benefits of these segmentation algorithms based on deformable models that integrate multiple sources of constraints. KeywordsMetamorphs-Active volume models-Deformable models-Implicit representation-Texture-Distance transform-Nonparametric region statistics-Multiple-surface constraint-Finite element method
    04/2011: pages 1-31;
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    ABSTRACT: In this paper, we propose a novel predictive model, active volume model (AVM), for object boundary extraction. It is a dynamic “object” model whose manifestation includes a deformable curve or surface representing a shape, a volumetric interior carrying appearance statistics, and an embedded classifier that separates object from background based on current feature information. The model focuses on an accurate representation of the foreground object's attributes, and does not explicitly represent the background. As we will show, however, the model is capable of reasoning about the background statistics thus can detect when is change sufficient to invoke a boundary decision. When applied to object segmentation, the model alternates between two basic operations: 1) deforming according to current region of interest (ROI), which is a binary mask representing the object region predicted by the current model, and 2) predicting ROI according to current appearance statistics of the model. To further improve robustness and accuracy when segmenting multiple objects or an object with multiple parts, we also propose multiple-surface active volume model (MSAVM), which consists of several single-surface AVM models subject to high-level geometric spatial constraints. An AVM's deformation is derived from a linear system based on finite element method (FEM). To keep the model's surface triangulation optimized, surface remeshing is derived from another linear system based on Laplacian mesh optimization (LMO) , . Thus efficient optimization and fast convergence of the model are achieved by solving two linear systems. Segmentation, validation and comparison results are presented from experiments on a variety of 2-D and 3-D medical images.
    IEEE Transactions on Medical Imaging 04/2011; · 4.03 Impact Factor
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    ABSTRACT: Network structures formed by actin filaments are present in many kinds of fluorescence microscopy images. In order to quantify the conformations and dynamics of such actin filaments, we propose a fully automated method to extract actin networks from images and analyze network topology. The method handles well intersecting filaments and, to some extent, overlapping filaments. First we automatically initialize a large number of Stretching Open Active Contours (SOACs) from ridge points detected by searching for plus-to-minus sign changes in the gradient map of the image. These initial SOACs then elongate simultaneously along the bright center-lines of filaments by minimizing an energy function. During their evolution, they may merge or stop growing, thus forming a network that represents the topology of the filament ensemble. We further detect junction points in the network and break the SOACs at junctions to obtain "SOAC segments". These segments are then re-grouped using a graph-cut spectral clustering method to represent the configuration of actin filaments. The proposed approach is generally applicable to extracting intersecting curvilinear structures in noisy images. We demonstrate its potential using two kinds of data: (1) actin filaments imaged by Total Internal Reflection Fluorescence Microscopy (TIRFM) in vitro; (2) actin cytoskeleton networks in fission yeast imaged by spinning disk confocal microscopy.
    Proceedings / IEEE International Symposium on Biomedical Imaging: from nano to macro. IEEE International Symposium on Biomedical Imaging 03/2011; 2011:1334-1340.
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    ABSTRACT: We introduce a novel algorithm for actin filament segmentation in 2D TIRFM image sequences. This problem is difficult because actin filaments dynamically change shapes during their growth, and the TIRFM images are usually noisy. We ask a user to specify the two tips of a filament of interest in the first frame. We then model the segmentation problem in an image sequence as a temporal chain, where its states are tip locations; given candidate tip locations, actin filaments' body points are inferred by a dynamic programming method, which adaptively generates candidate solutions. Combining candidate tip locations and their inferred body points, the temporal chain model is efficiently optimized using another dynamic programming method. Evaluation on noisy TIRFM image sequences demonstrates the accuracy and robustness of this approach.
    Information processing in medical imaging: proceedings of the ... conference 01/2011; 22:411-23.
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    ABSTRACT: Object boundary extraction is an important task in brain image analysis. Acquiring detailed 3D representations of the brain structures could improve the detection rate of diseases at earlier stages. Deformable model based segmentation methods have been widely used with considerable success. Recently, 3D Active Volume Model (AVM) was proposed, which incorporates both gradient and region information for robustness. However, the segmentation performance of this model depends on the position, size and shape of the initialization, especially for data with complex texture. Furthermore, there is no shape prior information integrated. In this paper, we present an approach combining AVM and Active Shape Model (ASM). Our method uses shape information from training data to constrain the deformation of AVM. Experiments have been made on the segmentation of complex structures of the rodent brain from MR images, and the proposed method performed better than the original AVM.
    Proceedings of the 8th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2011, March 30 - April 2, 2011, Chicago, Illinois, USA; 01/2011
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    ABSTRACT: 3D parametric deformable models have been used to extract volumetric object boundaries and they generate smooth boundary surfaces as results. However, in some segmentation cases, such as cerebral cortex with complex folds and creases, and human lung with high curvature boundary, parametric deformable models often suffer from over-smoothing or decreased mesh quality during model deformation. To address this problem, we propose a 3D Laplacian-driven parametric deformable model with a new internal force. Derived from a Mesh Laplacian, the internal force exerted on each control vertex can be decomposed into two orthogonal vectors based on the vertex's tangential plane. We then introduce a weighting function to control the contributions of the two vectors based on the model mesh's geometry. Deforming the new model is solving a linear system, so the new model can converge very efficiently. To validate the model's performance, we tested our method on various segmentation cases and compared our model with Finite Element and Level Set deformable models.
    IEEE International Conference on Computer Vision, ICCV 2011, Barcelona, Spain, November 6-13, 2011; 01/2011
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    ABSTRACT: In this paper, we propose a novel predictive model, active volume model (AVM), for object boundary extraction. It is a dynamic "object" model whose manifestation includes a deformable curve or surface representing a shape, a volumetric interior carrying appearance statistics, and an embedded classifier that separates object from background based on current feature information. The model focuses on an accurate representation of the foreground object's attributes, and does not explicitly represent the background. As we will show, however, the model is capable of reasoning about the background statistics thus can detect when is change sufficient to invoke a boundary decision. When applied to object segmentation, the model alternates between two basic operations: 1) deforming according to current region of interest (ROI), which is a binary mask representing the object region predicted by the current model, and 2) predicting ROI according to current appearance statistics of the model. To further improve robustness and accuracy when segmenting multiple objects or an object with multiple parts, we also propose multiple-surface active volume model (MSAVM), which consists of several single-surface AVM models subject to high-level geometric spatial constraints. An AVM's deformation is derived from a linear system based on finite element method (FEM). To keep the model's surface triangulation optimized, surface remeshing is derived from another linear system based on Laplacian mesh optimization (LMO). Thus efficient optimization and fast convergence of the model are achieved by solving two linear systems. Segmentation, validation and comparison results are presented from experiments on a variety of 2-D and 3-D medical images.
    IEEE Transactions on Medical Imaging 01/2011; 30:774-791. · 4.03 Impact Factor
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    ABSTRACT: In this paper, we propose a method to segment multiple rodent brain structures simultaneously. This method combines deformable models and hierarchical shape priors within one framework. The deformation module employs both gradient and appearance information to generate image forces to deform the shape. The shape prior module uses Principal Component Analysis to hierarchically model the multiple structures at both global and local levels. At the global level, the statistics of relative positions among different structures are modeled. At the local level, the shape statistics within each structure is learned from training samples. Our segmentation method adaptively employs both priors to constrain the intermediate deformation result. This prior constraint improves the robustness of the model and benefits the segmentation accuracy. Another merit of our prior module is that the size of the training data can be small, because the shape prior module models each structure individually and combines them using global statistics. This scheme can preserve shape details better than directly applying PCA on all structures. We use this method to segment rodent brain structures, such as the cerebellum, the left and right striatum, and the left and right hippocampus. The experiments show that our method works effectively and this hierarchical prior improves the segmentation performance.
    Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention. 01/2011; 14(Pt 3):611-8.
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    ABSTRACT: We use open active contours to quantify cytoskeletal structures imaged by fluorescence microscopy in two and three dimensions. We developed an interactive software tool for segmentation, tracking, and visualization of individual fibers. Open active contours are parametric curves that deform to minimize the sum of an external energy derived from the image and an internal bending and stretching energy. The external energy generates (i) forces that attract the contour toward the central bright line of a filament in the image, and (ii) forces that stretch the active contour toward the ends of bright ridges. Images of simulated semiflexible polymers with known bending and torsional rigidity are analyzed to validate the method. We apply our methods to quantify the conformations and dynamics of actin in two examples: actin filaments imaged by TIRF microscopy in vitro, and actin cables in fission yeast imaged by spinning disk confocal microscopy.
    Cytoskeleton 11/2010; 67(11):693-705. · 2.87 Impact Factor
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    ABSTRACT: We introduce a novel algorithm for actin filament segmentation in a 2D TIRFM image sequence. We treat the 2D time-lapse sequence as a 3D image volume and propose an over-grown active surface model to segment the body of a filament on all slices simultaneously. In order to locate the two ends of the filament on the over-grown surface, a novel 2D spatiotemporal domain is created based on the resulting surface. Two 2D active contour models deform in this domain to locate the two filament ends accurately. Evaluation on TIRFM image sequences with very low SNRs and comparison with a previous method demonstrate the accuracy and robustness of this approach.
    Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention. 01/2010; 13(Pt 1):86-94.
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    ABSTRACT: We present a novel method for 3D brain tumor volume segmentation based on a parallel cellular automata framework. Our method incorporates prior label knowledge gathered from user seed information to influence the cellular automata decision rules. Our proposed method is able to segment brain tumor volumes quickly and accurately using any number of label classifications. Exploiting the inherent parallelism of our algorithm, we adopt this method to the Graphics Processing Unit (GPU). Additionally, we introduce the concept of individual label strength maps to visualize the improvements of our method. As we demonstrate in our quantitative and qualitative results, the key benefits of our system are accuracy, robustness to complex structures, and speed. We compute segmentations nearly 45x faster than conventional CPU methods, enabling user feedback at interactive rates.
    IEEE 23rd International Symposium on Computer-Based Medical Systems (CBMS 2010), Perth, Australia, October 12-15, 2010; 01/2010
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    ABSTRACT: This paper presents an automated method for actin filament segmentation and tracking for measuring tip elongation rates in Total Internal Reflection Fluorescence Microscopy (TIRFM) images. The main contributions of the paper are: (i) we use a novel open active contour model for filament segmentation and tracking, which is fast and robust against noise; (ii) different strategies are proposed to solve the filament intersection problem, which is shown to be the main difficulty in filament tracking; and (iii) this fully automated method avoids the need of human interaction and thus reduces required time for the entire elongation measurement process on an image sequence. Application to experimental results demonstrated the robustness and effectiveness of this method.
    Proceedings / IEEE International Symposium on Biomedical Imaging: from nano to macro. IEEE International Symposium on Biomedical Imaging 06/2009; 2009:1302-1305.
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    ABSTRACT: In this paper, we introduce a novel algorithm to solve global shape registration problems. We use gray-scale "im-ages" to represent source shapes, and propose a novel two-component Gaussian Mixtures (GM) distance map repre-sentation for target shapes. Based on this flexible asym-metric image-based representation, a new energy function is defined. It proves to be a more robust shape dissimilarity metric that can be computed efficiently. Such high efficiency is essential for global optimization methods. We adopt one of them, the Particle Swarm Optimization (PSO), to effec-tively estimate the global optimum of the new energy func-tion. Experiments and comparison performed on general-ized shape data including continuous shapes, unstructured sparse point sets, and gradient maps, demonstrate the ro-bustness and effectiveness of the algorithm.
    06/2009;
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    ABSTRACT: We introduce a novel algorithm for actin filament tracking and elongation measurement. Particle Filters (PF) and Stretching Open Active Contours (SOAC) work cooperatively to simplify the modeling of PF in a one-dimensional state space while naturally integrating filament body constraints to tip estimation. Our algorithm reduces the PF state spaces to one-dimensional spaces by tracking filament bodies using SOAC and probabilistically estimating tip locations along the curve length of SOACs. Experimental evaluation on TIRFM image sequences with very low SNRs demonstrates the accuracy and robustness of this approach.
    Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention. 01/2009; 12(Pt 2):673-81.
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    ABSTRACT: In this paper, we propose a novel predictive model for object boundary, which can integrate information from any sources. The model is a dynamic ldquoobjectrdquo model whose manifestation includes a deformable surface representing shape, a volumetric interior carrying appearance statistics, and an embedded classifier that separates object from background based on current feature information. Unlike Snakes, Level Set, Graph Cut, MRF and CRF approaches, the model is ldquoself-containedrdquo in that it does not model the background, but rather focuses on an accurate representation of the foreground object's attributes. As we will show, however, the model is capable of reasoning about the background statistics thus can detect when is change sufficient to invoke a boundary decision. The shape of the 3D model is considered as an elastic solid, with a simplex-mesh (i.e. finite element triangulation) surface made of thousands of vertices. Deformations of the model are derived from a linear system that encodes external forces from the boundary of a Region of Interest (ROI), which is a binary mask representing the object region predicted by the current model. Efficient optimization and fast convergence of the model are achieved using the Finite Element Method (FEM). Other advantages of the model include the ease of dealing with topology changes and its ability to incorporate human interactions. Segmentation and validation results are presented for experiments on noisy 3D medical images.
    2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2009), 20-25 June 2009, Miami, Florida, USA; 01/2009
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    Tian Shen, Xiaolei Huang
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    ABSTRACT: In this paper, we propose Multiple-Surface Active Volume Models (MSAVM) to extract 3D objects from volumetric medical images. Being able to incorporate spatial constraints among multiple objects, MSAVM is more robust and accurate than the original Active Volume Models. The main novelty in MSAVM is that it has two surface-distance based functions to adaptively adjust the weights of contribution from the image-based region information and from spatial constraints among multiple interacting surfaces. These two functions help MSAVM not only overcome local minima but also avoid leakage. Because of the implicit representation of AVM, the spatial information can be calculated based on the model's signed distance transform map with very low extra computational cost. The MSAVM thus has the efficiency of the original 3D AVM but produces more accurate results. 3D segmentation results, validation and comparison are presented for experiments on volumetric medical images.
    Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention. 01/2009; 12(Pt 2):1059-66.
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    ABSTRACT: A new user interaction method called interactive polygons is presented in this paper. These interaction polygons are de- signed for use with the Active Volume Model segmentation method (1), which deforms with constraints from both Region Of Interest (ROI) and image gradient information. The two kinds of interaction polygons we apply are "merge polygons" and "split polygons" which identify the foreground and back- ground, respectively. Users are allowed to draw these poly- gons to correct the original AVM segmentation results. These interactive polygons are used to update the region statisti cs in the original model and help the model deform to the desirable boundaries.
    Proceedings of the 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, Boston, MA, USA, June 28 - July 1, 2009; 01/2009