Gang Dong

Intravital leukocyte detection using the gradient inverse coefficient of variation

The problem of identifying and counting rolling leukocytes within intravital microscopy is of both theoretical and practical interest. Currently, methods exist for tracking rolling leukocytes in vivo, but these methods rely on manual detection of the cells. In this paper we propose a technique for accurately detecting rolling leukocytes based on Bayesian classification. The classification depends on a feature score, the gradient inverse coefficient of variation (GICOV), which serves to discriminate rolling leukocytes from a cluttered environment. The leukocyte detection process consists of three sequential steps: the first step utilizes an ellipse matching algorithm to coarsely identify the leukocytes by finding the ellipses with a locally maximal GICOV. In the second step, starting from each of the ellipses found in the first step, a B-spline snake is evolved to refine the leukocytes boundaries by maximizing the associated GICOV score. The third and final step retains only the extracted contours that have a GICOV score above the analytically determined threshold. Experimental results using 327 rolling leukocytes were compared to those of human experts and currently used methods. The proposed GICOV method achieves 78.6% leukocyte detection accuracy with 13.1% false alarm rate.

On the Convergence of Bilateral Filter for Edge-Preserving Image Smoothing

The bilateral filter represents a wide group of nonlinear filters for edge-preserving image smoothing. In this work, we study the convergence properties of the bilateral filter algorithm. The understanding is established that the bilateral filter is an optimization procedure. We demonstrate that the bilateral filter is equivalent to minimizing a robust cost criterion using iterative reweighting, which is a good approximation to the very fast but unstable Newtons method. Further, the results of the analysis allow us to derive an improved hybrid smoothing scheme with concerns of computational efficiency and edge preservation.

Detection of rolling leukocytes by marked point processes

The marked point process (MPP) provides a useful and theoretically well-established tool for integrating spatial information into the image analysis process. We consider the problem of detecting rolling leukocytes within intravital microscopy images. A first stage of the detection method reduces the detection to a set of points, each one representing a possible leukocyte. Our task is then to decide which points are actual leukocytes. We propose an MPP-based approach that aims at improving both the accuracy and efficiency of the detection process by means of exploiting the spatial interrelationships. We construct a Markov chain Monte Carlo algorithm to obtain the maximum a posteriori (MAP) estimation of a set of points corresponding to the centroids of leukocytes observed in the image. The optimal solution, in terms of the MAP principle, is computed with respect to all leukocytes, rather than a single leukocyte. A quantitative study of our detection approach demonstrates results that compare very well to those achieved by manual detection and exceed the solution quality given by two competing methods. Our approach can serve as a fully automated substitute to the tedious and time-consuming manual rolling leukocyte detection process.

Tracking multiple cells by correspondence resolution in a sequential Bayesian framework

We propose a multi-target tracking (MTT) algorithm in a sequential Bayesian framework that computes cell velocities from video microscopy. Unlike the traditional tracking methods, our formulation does not involve the estimation of target states; instead, we estimate one-to-one target correspondences by way of a sequential Markov chain Monte Carlo (MCMC) algorithm. The proposed probabilistic framework also automatically accounts for a variable number of targets. We have tested the proposed tracking algorithm on two different in vitro and one in vivo microscopy experiments. The three experiments show that the method holds promise in terms of low false positive and false negative rates as well as low rates of correspondence error.

A variational method for leukocyte detection

In this paper, we propose a variational method for the detection of leukocytes observed in vivo. An adaptive threshold surface is constructed automatically using boundary information from the image. The surface is created using an objective functional that is minimized via a variational approach. This surface is constrained by an edge field that is also computed with a variational method. Objects extracted from background are pruned according to two geometric criteria. In the experiments, we find the false positive rate of the detector and show that the proposed approach can automatically and accurately identify multiple rolling leukocytes in vivo.

Object Identification by Marked Point Process

In this paper, we propose an algorithm for the identification of objects from a noisy and cluttered background in video sequences. Our algorithm is based on the marked point process (MPP) framework, which provides a useful tool for integrating object spatial information into the identification process. The maximum a posteriori (MAP) estimation of a set of points corresponding to the centroids of objects observed in the image is obtained via a Markov chain Monte Carlo algorithm. The optimal solution, in terms of the MAP principle, is computed with respect to all objects in the scene, rather than single objects. The algorithm is applied to real data: intravital microscopic rolling leukocyte video datasets. A quantitative study of our approach demonstrates that the proposed approach can serve as a fully automated substitute to the tedious manual rolling leukocyte detection process

Automated leukocyte detection in VIVO

In this paper we propose a technique for detecting rolling leukocytes within intravital microscopy based on Bayesian classification. The classification depends on a feature score, the gradient inverse coefficient of variation (GICOV), which serves to discriminate rolling leukocytes from a cluttered environment. An ellipse matching algorithm and B-spline snake for the aim of boundary extraction are discussed. The extracted contours that have a GICOV score above the analytically determined threshold are retained as identified objects. Experimental results using 327 rolling leukocytes were compared to those of human experts and currently used methods.

Evaluation of intravital tracking algorithms

We are exploring automated tracking for cells observed in vivo by video microscopy. In the past, three tracking algorithms have been utilized for tracking cells: the normalized cross correlation tracking algorithm, the centroid tracking algorithm and the snake based tracking algorithm. This paper focuses on the analysis, evaluation and comparison of the above three tracking algorithms using some performance measures. These performance measures include: (1) the average number of consecutive frames tracked; (2) the localization error; (3) correlation with ground truth, and (4) tracking time. One hundred in vivo (intravital) videos were used in our experiments for analysis, evaluation and comparison. The results show that snake based tracking provides the best performance, and that automated tracking of leukocytes is indeed possible.

Detection of microspheres in venules for automated particle image velocimetry

In this paper, we propose an automatic approach for detecting particle tracers (microspheres) in microscopic imagery obtained from mouse cremaster venules in vivo. Measurements of the translational speed and radial position of individual microspheres provide the input data needed to extract velocity profiles from steady blood flow in venules. These profiles provide information about local hemodynamics that is critical to a broad range of fields in microvascular physiology, including endothelial-cell mechanotransduction, inflammation, and microvascular resistance. In the preprocessing stage, an active contour method based on dynamic programming is used for vessel region extraction. Each microsphere is then identified using a process of coarse segmentation followed by verification. Segmentation is achieved using a morphological method for microsphere detection while verification is achieved using an analytical model tailored to the microsphere. Experimental results are obtained using the proposed scheme and compared with previously published manually acquired data.

Detection of Microspheres in Venules for Automated Particle Image

In this paper, we propose an automatic approach for detecting particle tracers (microspheres) in microscopic imagery obtained from mouse cremaster venules in vivo.Measurements of the translational speed and radial position of individual microspheresprovide the input data needed to extract velocity profiles from steady blood flow in venules.These profiles provide information about local hemodynamics that is critical to a broadrange of fields in microvascular physiology, including endothelial-cell mechanotransduction,inflammation, and microvascular resistance. In the preprocessing stage, an active contourmethod based on dynamic programming is used for vessel region extraction. Eachmicrosphere is then identified using a process of coarse segmentation followed byverification. Segmentation is achieved using a morphological method for microspheredetection while verification is achieved using an analytical model tailored to the microsphere.Experimental results are obtained using the proposed scheme and compared with previouslypublished manually acquired data.