@article {4310, title = {Anisotropic path searching for automatic neuron reconstruction.}, journal = {Medical Image Analysis}, volume = {15}, year = {2011}, month = {2011 Oct}, pages = {680-9}, abstract = {

Full reconstruction of neuron morphology is of fundamental interest for the analysis and understanding of their functioning. We have developed a novel method capable of automatically tracing neurons in three-dimensional microscopy data. In contrast to template-based methods, the proposed approach makes no assumptions about the shape or appearance of neurite structure. Instead, an efficient seeding approach is applied to capture complex neuronal structures and the tracing problem is solved by computing the optimal reconstruction with a weighted graph. The optimality is determined by the cost function designed for the path between each pair of seeds and by topological constraints defining the component interrelations and completeness. In addition, an automated neuron comparison method is introduced for performance evaluation and structure analysis. The proposed algorithm is computationally efficient and has been validated using different types of microscopy data sets including Drosophila{\textquoteright}s projection neurons and fly neurons with presynaptic sites. In all cases, the approach yielded promising results.

}, issn = {1361-8423}, doi = {10.1016/j.media.2011.05.013}, author = {Xie, Jun and Zhao, Ting and Lee, Tzumin and Myers, Eugene and Peng, Hanchuan} } @article {2700, title = {Automated reconstruction of neuronal morphology based on local geometrical and global structural models.}, journal = {Neuroinformatics}, volume = {9}, year = {2011}, month = {2011 May 6}, pages = {247-61}, abstract = {

Digital reconstruction of neurons from microscope images is an important and challenging problem in neuroscience. In this paper, we propose a model-based method to tackle this problem. We first formulate a model structure, then develop an algorithm for computing it by carefully taking into account morphological characteristics of neurons, as well as the image properties under typical imaging protocols. The method has been tested on the data sets used in the DIADEM competition and produced promising results for four out of the five data sets.

}, issn = {1559-0089}, doi = {10.1007/s12021-011-9120-3}, author = {Zhao, Ting and Xie, Jun and Amat, Fernando and Clack, Nathan and Ahammad, Parvez and Peng, Hanchuan and Long, Fuhui and Myers, Eugene} } @article {2698, title = {Automatic neuron tracing in volumetric microscopy images with anisotropic path searching.}, journal = {Medical Image Computing and Computer-Assisted Intervention: MICCAI International Conference on Medical Image Computing and Computer-Assisted Intervention}, volume = {13}, year = {2010}, month = {2010}, pages = {472-9}, abstract = {

Full reconstruction of neuron morphology is of fundamental interest for the analysis and understanding of neuron function. We have developed a novel method capable of tracing neurons in three-dimensional microscopy data automatically. In contrast to template-based methods, the proposed approach makes no assumptions on the shape or appearance of neuron{\textquoteright}s body. Instead, an efficient seeding approach is applied to find significant pixels almost certainly within complex neuronal structures and the tracing problem is solved by computing an graph tree structure connecting these seeds. In addition, an automated neuron comparison method is introduced for performance evaluation and structure analysis. The proposed algorithm is computationally efficient. Experiments on different types of data show promising results.

}, keywords = {Algorithms, Animals, Anisotropy, Artificial Intelligence, Automated, Cells, Computer-Assisted, Cultured, Humans, Image Enhancement, Image Interpretation, Imaging, Microscopy, Neurons, Pattern Recognition, Reproducibility of Results, Sensitivity and Specificity, Three-Dimensional}, author = {Xie, Jun and Zhao, Ting and Lee, Tzumin and Myers, Eugene and Peng, Hanchuan} } @article {1495, title = {Automatic tracking of Escherichia coli in phase-contrast microscopy video.}, journal = {IEEE Transactions on Bio-Medical Engineering}, volume = {56}, year = {2009}, month = {2009 Feb}, pages = {390-9}, abstract = {

In this paper, we present an automatic method for estimating the trajectories of Escherichia coli bacteria from in vivo phase-contrast microscopy. To address the low-contrast boundaries in cellular images, an adaptive kernel-based technique is applied to detect cells in each frame. In addition to intensity features, region homogeneity measure and class uncertainty are also applied in this detection technique. To track cells with complex motion, a novel matching gain measure is introduced to cope with the challenges, particularly the presence of low-contrast boundary, the variations of appearance, and the frequent overlapping and occlusion. For multicell tracking over time, an optimal matching strategy is introduced to improve the handling of cell collision and broken trajectories. The results of successful tracking of Escherichia coli from various phase-contrast sequences are reported and compared with manually determined trajectories, as well as those obtained from existing tracking schemes. The stability of the algorithm with different parameter values is also analyzed and discussed.

}, keywords = {Algorithms, Artificial Intelligence, Automated, Chemotaxis, Computer-Assisted, Entropy, Escherichia coli, Image Processing, Microscopy, Pattern Recognition, Phase-Contrast, Video}, issn = {1558-2531}, doi = {10.1109/TBME.2008.2005956}, author = {Xie, Jun and Khan, Shahid and Shah, Mubarak} } @article {1496, title = {Automatic tracking of Escherichia coli bacteria.}, journal = {Medical Image Computing and Computer-Assisted Intervention: MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention}, volume = {11}, year = {2008}, month = {2008 Sep 6}, pages = {824-32}, abstract = {

In this paper, we present an automatic method for estimating the trajectories of Escherichia coli bacteria from in vivo phase-contrast microscopy videos. To address the low-contrast boundaries in cellular images, an adaptive kernel-based technique is applied to detect cells in sequence of frames. Then a novel matching gain measure is introduced to cope with the challenges such as dramatic changes of cells{\textquoteright} appearance and serious overlapping and occlusion. For multiple cell tracking, an optimal matching strategy is proposed to improve the handling of cell collision and broken trajectories. The results of successful tracking of Escherichia coli from various phase-contrast sequences are reported and compared with manually-determined trajectories, as well as those obtained from existing tracking methods. The stability of the algorithm with different parameter values is also analyzed and discussed.

}, keywords = {Algorithms, Artificial Intelligence, Automated, Cell Movement, Cluster Analysis, Computer-Assisted, Escherichia coli, Image Enhancement, Image Interpretation, Microscopy, Pattern Recognition, Phase-Contrast, Reproducibility of Results, Sensitivity and Specificity, Subtraction Technique, Video}, author = {Xie, Jun and Khan, Shahid and Shah, Mubarak} } @article {1492, title = {Shape registration by optimally coding shapes.}, journal = {IEEE Transactions on Information Technology in Biomedicine : A Publication of the IEEE Engineering in Medicine and Biology Society}, volume = {12}, year = {2008}, month = {2008 Sep}, pages = {627-35}, abstract = {

This paper formulates shape registration as an optimal coding problem. It employs a set of landmarks to establish the correspondence between shapes, and assumes that the best correspondence can be achieved when the polygons formed by the landmarks optimally code all the shape contours, i.e., obtain their minimum description length (MDL). This is different from previous MDL-based shape registration methods, which code the landmark locations. In this paper, each contour is discretized to be a set of points to make the coding feasible, and a number of strategies are adopted to tackle the difficult optimization problem involved. The resulting algorithm, called CAP, is able to yield statistical shape model with better quality in terms of model generalization error, which is demonstrated on both synthetic and biomedical shapes.

}, keywords = {Algorithms, Artificial Intelligence, Automated, Computer-Assisted, Image Enhancement, Image Interpretation, Pattern Recognition, Reproducibility of Results, Sensitivity and Specificity}, issn = {1558-0032}, doi = {10.1109/TITB.2008.920798}, author = {Jiang, Yifeng and Xie, Jun and Tsui, Hung-Tat} } @conference {6532, title = {Unfolding warping for object recognition.}, booktitle = {19TH International Conference on Pattern Recognition}, year = {2008}, month = {2008 December 11}, publisher = {IEEE}, organization = {IEEE}, address = {Tampa, FL}, abstract = {

In practice, understanding the spatial relationships between the surfaces of an object, can significantly improve the performance of object recognition systems. In this paper we propose a novel framework to recognize objects in pictures taken from arbitrary viewpoints. The idea is to maintain the frontal views of the major faces of objects in a global flat map. Then an unfolding warping technique is used to change the pose of the query object in the test view so that all visible surfaces of the object can be observed from a frontal viewpoint, improving the handling of serious occlusions and large viewpoint changes. We demonstrate the effectiveness of our approach through analysis of recognition trials of complex objects with comparison to popular methods.

}, isbn = {978-1-4244-2174-9}, doi = {10.1109/ICPR.2008.4761188}, author = {Xie, Jun and Hu, Min and Shah, Mubarak} } @article {1490, title = {Boundary enhancement and speckle reduction for ultrasound images via salient structure extraction.}, journal = {IEEE Transactions on Bio-Medical Engineering}, volume = {53}, year = {2006}, month = {2006 Nov}, pages = {2300-9}, abstract = {

In this paper, we present an approach for medical ultrasound (US) image enhancement. It is based on a novel perceptual saliency measure which favors smooth, long curves with constant curvature. The perceptual salient boundaries of tissues in US images are enhanced by computing the saliency of directional vectors in the image space, via a local searching algorithm. Our measure is generally determined by curvature changes, intensity gradient and the interaction of neighboring vectors. To restrain speckle noise during the enhancement process, an adaptive speckle suspension term is also combined into the proposed saliency measure. The results obtained on both simulated images and medical US data reveal superior performance of the novel approach over a number of commonly used speckle filters. Applications of US image segmentation show that although the proposed algorithm cannot remove the speckle noise completely and may discard weak anatomical structures in some case, it still provides a considerable gain to US image processing for computer-aided diagnosis.

}, keywords = {Algorithms, Automated, Computer-Assisted, Humans, Image Enhancement, Image Interpretation, Pattern Recognition, Reproducibility of Results, Sensitivity and Specificity, Ultrasonography}, issn = {0018-9294}, doi = {10.1109/TBME.2006.878088}, author = {Xie, Jun and Jiang, Yifeng and Tsui, Hung-Tat and Heng, Pheng-Ann} } @article {1489, title = {Image diffusion using saliency bilateral filter.}, journal = {Medical Image Computing and Computer-Assisted Intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention}, volume = {9}, year = {2006}, month = {2006}, pages = {67-75}, abstract = {

Image diffusion can smooth away noise and small-scale structures while retaining important features, thereby enhancing the performances of many image processing algorithms such as image compression, segmentation and recognition. In this paper, we present a novel diffusion algorithm for which the filtering kernels vary according to the perceptual saliency of boundaries in the input images. The boundary saliency is estimated through a saliency measure which is generally determined by curvature changes, intensity gradient and the interaction of neighboring vectors. The connection between filtering kernels and perceptual saliency makes it possible to remove small-scale structures and preserves significant boundaries adaptively. The effectiveness of the proposed approach is validated by experiments on various medical images including the color Chinese Visible Human data set and gray MRI brain images.

}, keywords = {Algorithms, Artificial Intelligence, Automated, Brain, Cluster Analysis, Computer-Assisted, Diffusion Magnetic Resonance Imaging, Humans, Image Enhancement, Image Interpretation, Magnetic Resonance Imaging, Pattern Recognition, Reproducibility of Results, Sensitivity and Specificity}, author = {Xie, Jun and Heng, Pheng-Ann and Ho, Simon S M and Shah, Mubarak} }