@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} } @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 {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} }