Globally Optimal Active Contours, Sequential Monte Carlo and On-Line Learning for Vessel Segmentation

Florin, Charles; Paragios, Nikos; Williams, Jim

doi:10.1007/11744078_37

Cited by 33 publications

(22 citation statements)

References 21 publications

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“…A limitation of the latter methods is that they do not update the region statistics during the model evolution, and therefore local feature variations are difficult to be captured. Region updating is proposed in [13], where active contours with particle filtering is used for vascular segmentation. The leftmost image shows the original grayscale image, while the second from left image shows the ground-truth, i.e., the actual boundaries of RV and LV, with two red closed lines; these boundaries were obtained by manual segmentation.…”

Section: Parametric Deformable Modelsmentioning

confidence: 99%

Medical image segmentation

Jones¹

2015

2015 International Conference on Image Processing Theory, Tools and Applications (IPTA)

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Automated image segmentation, which aims at automated extraction of object boundary features, plays a fundamental role in understanding image content for searching and mining in medical image archives. A challenging problem is to segment regions with boundary insufficiencies, i.e., missing edges and/or lack of texture contrast between regions of interest (ROIs) and background. To address this problem, several segmentation approaches have been proposed in the literature, with many of them providing rather promising results.In this chapter we focus on two general categories of segmentation methods widely used in medical vision, namely the deformable models-and the machine learning-based classification approaches. We first review these two categories of methods and discuss the potential of integrating the two approaches, and then we detail on two of the most recent methods for medical image segmentation: (i) the Metamorphs, a semi-parametric deformable model that combines appearance and shape into a unified space, and (ii) the integration of geometric models with a collaborative formulation of CRFs in a probabilistic segmentation framework. We show different examples of medical data segmentation, we draw general conclusions from the methods described in this chapter, and we give future directions for solving challenging and open problems in medical image segmentation.

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Section: Parametric Deformable Modelsmentioning

confidence: 99%

Medical image segmentation

Jones¹

2015

2015 International Conference on Image Processing Theory, Tools and Applications (IPTA)

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“…Vessel segmentation algorithms extract parameters including centerlines, edges and junctions and the whole vascular network (Kirbas and Quek, 2000;Lesage et al, 2009). Techniques applied include pattern recognition techniques (Zana and Klein, 2001;Truc et al, 2009), model-based approaches (Agin and Binford, 1976;Pellot et al, 1994) and tracking-based approaches in the spatial frame (Florin et al, 2006;Wörz and Rohr, 2007).…”

Section: Related Workmentioning

confidence: 99%

A Bayesian filtering approach to incorporate 2D/3D time-lapse confocal images for tracking angiogenic sprouting cells interacting with the gel matrix

Ong

Dauwels

Ang

et al. 2014

Medical Image Analysis

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We present a new approach to incorporating information from heterogeneous images of migrating cells in 3D gel. We study 3D angiogenic sprouting, where cells burrow into the gel matrix, communicate with other cells and create vascular networks. We combine time-lapse fluorescent images of stained cell nuclei and transmitted light images of the background gel to track cell trajectories. The nuclei images are sampled less frequently due to photo toxicity. Hence, 3D cell tracking can be performed more reliably when 2D sprout profiles, extracted from gel matrix images, are effectively incorporated. We employ a Bayesian filtering approach to optimally combine the two heterogeneous images with different sampling rates. We construct stochastic models to predict cell locations and sprout profiles and condition the likelihood of nuclei location by the sprout profile. The conditional distribution is non-Gaussian and the cell dynamics is non-linear. To jointly update cell and sprout estimates, we use a Rao-Blackwell particle filter. Simulation and experimental results show accurate tracking of multiple cells along with sprout formation, demonstrating synergistic effects of incorporating the two types of images.

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“…An adaptive boosting (AdaBoost) [11] machine learning algorithm was chosen for this task for its strong theoretical basis and ability to concurrently select and combine relevant features from the feature set during the training of each independent classifier.…”

Section: Probabilistic Branching Node Inferencementioning

confidence: 99%

Probabilistic branching node detection using AdaBoost and hybrid local features

Nuzhnaya

Barnathan

Ling

et al. 2010

2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro

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Probabilistic branching node inference is an important step for analyzing branching patterns involved in many anatomic structures. Based on an approach we have developed previously, we investigate combining machine learning techniques and hybrid image statistics for probabilistic branching node inference, using adaptive boosting as a probabilistic inference framework. Then, we use local image statistics at different image scales for feature representation, including the Harris cornerness, Laplacian, eigenvalues of the Hessian, and Harralick texture features. The proposed approach is applied to a breast imaging dataset consisting of 30 images, 7 of which were previously reported. The use of boosting and the Harralick texture feature further improves upon our previous results, highlighting the role of texture in the analysis of the breast ducts and other branching structures.Index Terms-Branching Structure, Breast Imaging, AdaBoost.

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Globally Optimal Active Contours, Sequential Monte Carlo and On-Line Learning for Vessel Segmentation

Cited by 33 publications

References 21 publications

Medical image segmentation

Medical image segmentation

A Bayesian filtering approach to incorporate 2D/3D time-lapse confocal images for tracking angiogenic sprouting cells interacting with the gel matrix

Probabilistic branching node detection using AdaBoost and hybrid local features

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