Variable Length Open Contour Tracking Using a Deformable Trellis

Sargin, M.E.; Altınok, Alphan; Manjunath, B. S.; Rose, Kenneth

doi:10.1109/tip.2010.2081680

Cited by 15 publications

(20 citation statements)

References 31 publications

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“…2(a), following the paradigm of [4]. Given frame t in the image sequence, together with the initial contourĈt of an object of interest, we deformĈt to the estimated contourCt.…”

Section: Deformable Trellis and Hidden Markov Modelmentioning

confidence: 99%

“…The probabilistic nature of HMM allows easy integration of multiple cues from observations, and enables computationally feasible global optimization via the efficient dynamic programming algorithm [3]. In [4], an enhanced HMM technique was proposed for tracking open-contour objects in biological image sequences, such as microtubules. More recently, [5] added a part-based representation to a generalized HMM framework for closed-contour face tracking and achieved higher accuracy.…”

Section: Introductionmentioning

confidence: 99%

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Hidden Markov models for tracking neuronal structure contours in electron micrograph stacks

Shih

Rose

2012

2012 9th IEEE International Symposium on Biomedical Imaging (ISBI)

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This paper is focused on the problem of tracking cell contours across an electron micrograph stack, so as to discern the 3D neuronal structures, with particular application to analysis of retinal images. While the problem bears similarity to traditional object tracking in video sequences, it poses additional significant challenges due to the coarse z-axis resolution which causes large contour deformations across frames, and involves major topological changes including contour splits and merges. The method proposed herein applies a deformable trellis, on which a hidden Markov model is defined, to track contour deformation. The first phase produces an estimated new contour and computes its probability given the model. The second phase detects low-confidence contour segments and tests the hypothesis that a topological change has occurred, by introducing corresponding hypothetical arcs and re-optimizing the contour. The most probable solution, including the topological hypothesis, is identified. Experimental results show, both quantitatively and qualitatively, that the proposed approach can effectively and efficiently track cell contours while accounting for splitting, merging, large contour displacements and deformations.

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“…2(a), following the paradigm of [4]. Given frame t in the image sequence, together with the initial contourĈt of an object of interest, we deformĈt to the estimated contourCt.…”

Section: Deformable Trellis and Hidden Markov Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Hidden Markov models for tracking neuronal structure contours in electron micrograph stacks

Shih

Rose

2012

2012 9th IEEE International Symposium on Biomedical Imaging (ISBI)

Self Cite

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“…In [7], significant tracking A specific example comparing output of [3] (on the left) versus the proposed approach (on the right). The ground truth is annotated in red, while green represents the tracker output performance improvement was obtained by employing an HMM approach, which has natural capability to probabilistically account for growth and shortening of the MT, and is computationally efficient due to the applicability of the Viterbi algorithm.…”

Section: Prior Workmentioning

confidence: 99%

“…A first important distinction with the deformable trellis of [10] and [7], is that here the trellis (and HMM) is overlaid on, and processes the probability map, rather than the original image itself. The intuition behind this paradigm is that it allows us to leverage the factor graph capability to separate the contours of interest from the clutter of other contours.…”

Section: Deformable Trellis and Hidden Markov Modelmentioning

confidence: 99%

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Deformable trellises on factor graphs for robust microtubule tracking in clutter

Kidambi

Shih

Rose

2012

2012 9th IEEE International Symposium on Biomedical Imaging (ISBI)

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A main challenge in microtubule tracking is due to clutter, or the presence of many similar intersecting structures. This paper proposes a two-layered probabilistic formulation which has at its foundation a factor graph serving as a multi-label inference engine designed to provide distinction between open contours of interest and other microtubules or noise. The second layer is a deformable trellis defined over the resulting label probability map, where a Hidden Markov Model (HMM) is employed to determine the most probable current location of the microtubule body. The overall framework enjoys the "best of both worlds" -the factor graph is effective in discriminating between contours of interest and others that exhibit similar statistical properties, while the deformable trellis with its HMM offer accurate modeling of microtubule dynamics in terms of growth and shortening, as well as precise body tracing, accounting for prior information, all within a principled Bayesian framework. Simulation results provide evidence that the proposed approach outperforms existing techniques.

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Coupled s‐excess HMM for vessel border tracking and segmentation

Essa

Jones

Xie

2019

Numer Methods Biomed Eng

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In this paper, we present a novel image segmentation technique, based on hidden Markov model (HMM), which we then apply to simultaneously segment interior and exterior walls of fluorescent confocal images of lymphatic vessels. Our proposed method achieves this by tracking hidden states, which are used to indicate the locations of both the inner and outer wall borders throughout the sequence of images. We parameterize these vessel borders using radial basis functions (RBFs), thus enabling us to minimize the number of points we need to track as we progress through multiple layers and therefore reduce computational complexity. Information about each border is detected using patch‐wise convolutional neural networks (CNN). We use the softmax function to infer the emission probability and use a proposed new training algorithm based on s‐excess optimization to learn the transition probability. We also introduce a new optimization method to determine the optimum sequence of the hidden states. Thus, we transform the segmentation problem into one that minimizes an s‐excess graph cut, where each hidden state is represented as a graph node and the weight of these nodes are defined by their emission probabilities. The transition probabilities are used to define relationships between neighboring nodes in the constructed graph. We compare our proposed method to the Viterbi and Baum–Welch algorithms. Both qualitative and quantitative analysis show superior performance of the proposed methods.

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Variable Length Open Contour Tracking Using a Deformable Trellis

Cited by 15 publications

References 31 publications

Hidden Markov models for tracking neuronal structure contours in electron micrograph stacks

Hidden Markov models for tracking neuronal structure contours in electron micrograph stacks

Deformable trellises on factor graphs for robust microtubule tracking in clutter

Coupled s‐excess HMM for vessel border tracking and segmentation

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