Robust Markov Random Field outlier detection and removal in subsampled images

McCool, Paul; Altmann, Yoann; Perperidis, Antonios; McLaughlin, Steve

doi:10.1109/ssp.2016.7551766

Cited by 5 publications

(5 citation statements)

References 11 publications

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“…We also utilized a probability-based outlier detection algorithm to clean KFD data, similar to the method proposed by McCool et al ( McCool et al, 2016 ). Initially, the KFD was calculated without explicitly checking the data quality.…”

Section: Methodsmentioning

confidence: 99%

Robust arterial compliance estimation with Katz’s fractal dimension of photoplethysmography

Xing,

Hong,

Alastruey

et al. 2024

Front. Physiol.

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Arterial compliance (AC) plays a crucial role in vascular aging and cardiovascular disease. The ability to continuously estimate aortic AC or its surrogate, pulse pressure (PP), through wearable devices is highly desirable, given its strong association with daily activities. While the single-site photoplethysmography (PPG)-derived arterial stiffness indices show reasonable correlations with AC, they are susceptible to noise interference, limiting their practical use. To overcome this challenge, our study introduces a noise-resistant indicator of AC: Katz’s fractal dimension (KFD) of PPG signals. We showed that KFD integrated the signal complexity arising from compliance changes across a cardiac cycle and vascular structural complexity, thereby decreasing its dependence on individual characteristic points. To assess its capability in measuring AC, we conducted a comprehensive evaluation using both in silico studies with 4374 virtual human data and real-world measurements. In the virtual human studies, KFD demonstrated a strong correlation with AC (r = 0.75), which only experienced a slight decrease to 0.66 at a signal-to-noise ratio of 15dB, surpassing the best PPG-morphology-derived AC measure (r = 0.41) under the same noise condition. In addition, we observed that KFD’s sensitivity to AC varied based on the individual’s hemodynamic status, which may further enhance the accuracy of AC estimations. These in silico findings were supported by real-world measurements encompassing diverse health conditions. In conclusion, our study suggests that PPG-derived KFD has the potential to continuously and reliably monitor arterial compliance, enabling unobtrusive and wearable assessment of cardiovascular health.

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Section: Methodsmentioning

confidence: 99%

Robust arterial compliance estimation with Katz’s fractal dimension of photoplethysmography

Xing,

Hong,

Alastruey

et al. 2024

Front. Physiol.

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“…However, the underlying covariance structure needs to consider too many neighbouring points to attain sufficient smoothing, which involves a prohibitive computational load. In order to obtain similar results with a lower computational burden, we propose to exploit the connected-surface structure to define a nearest neighbour Gaussian Markov random field (GMRF), similar to the one used by McCool et al in [31]. First, we alleviate the difficulties induced by the positivity constraint of the intensity values by introducing the following change of variables, which is a standard choice in spatial statistics dealing with Poisson noise (see [39,Chapter 4]) m n = log(r n ) n = 1, .…”

Section: Intensity Prior Modelmentioning

confidence: 99%

“…Moreover, the combination of these two processes implicitly defines a connected-surface structure that is used to efficiently sample the posterior distribution. To promote smoothness between reflectivities of points in the same surface, we define a nearest neighbour Gaussian Markov random field (GMRF) prior model, similar to the one proposed in [31]. Inference about the posterior distribution of points, their marks and the background level is done by an RJ-MCMC algorithm [11,Chapter 9], with carefully tailored moves to obtain high acceptance rates, ensuring better mixing and faster convergence rate.…”

Section: Introductionmentioning

confidence: 99%

Bayesian 3D Reconstruction of Complex Scenes from Single-Photon Lidar Data

Tachella¹,

Altmann²,

Ren³

et al. 2019

SIAM J. Imaging Sci.

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Light detection and ranging (Lidar) data can be used to capture the depth and intensity profile of a 3D scene. This modality relies on constructing, for each pixel, a histogram of time delays between emitted light pulses and detected photon arrivals. In a general setting, more than one surface can be observed in a single pixel. The problem of estimating the number of surfaces, their reflectivity and position becomes very challenging in the low-photon regime (which equates to short acquisition times) or relatively high background levels (i.e., strong ambient illumination). This paper presents a new approach to 3D reconstruction using single-photon, single-wavelength Lidar data, which is capable of identifying multiple surfaces in each pixel. Adopting a Bayesian approach, the 3D structure to be recovered is modelled as a marked point process and reversible jump Markov chain Monte Carlo (RJ-MCMC) moves are proposed to sample the posterior distribution of interest. In order to promote spatial correlation between points belonging to the same surface, we propose a prior that combines an area interaction process and a Strauss process. New RJ-MCMC dilation and erosion updates are presented to achieve an efficient exploration of the configuration space. To further reduce the computational load, we adopt a multiresolution approach, processing the data from a coarse to the finest scale. The experiments performed with synthetic and real data show that the algorithm obtains better reconstructions than other recently published optimization algorithms for lower execution times.

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“…Outlier/anomaly detection problems can usually be addressed using unsupervised or supervised methods [1]. In unsupervised approaches, the objects/anomalies to be detected are learned from the data by fitting them with suitable distributions without using explicitly-provided labels [2][3][4][5][6][7][8][9][10]. On the other hand, considering supervised approaches, the dataset is usually divided into training and testing sets.…”

Section: Introductionmentioning

confidence: 99%

Patch-Based Sparse Representation For Bacterial Detection

Eldaly

Altmann

Akram

et al. 2019

2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019)

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In this paper, we propose an unsupervised approach for bacterial detection in optical endomicroscopy images. This approach splits each image into a set of overlapping patches and assumes that observed intensities are linear combinations of the actual intensity values associated with background image structures, corrupted by additive Gaussian noise and potentially by a sparse outlier term modelling anomalies (which are considered to be candidate bacteria). The actual intensity term representing background structures is modelled as a linear combination of a few atoms drawn from a dictionary which is learned from bacteria-free data and then fixed while analyzing new images. The bacteria detection task is formulated as a minimization problem and an alternating direction method of multipliers (ADMM) is then used to estimate the unknown parameters. Simulations conducted using two ex vivo lung datasets show good detection and correlation performance between bacteria counts identified by a trained clinician and those of the proposed method.

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Robust Markov Random Field outlier detection and removal in subsampled images

Cited by 5 publications

References 11 publications

Robust arterial compliance estimation with Katz’s fractal dimension of photoplethysmography

Robust arterial compliance estimation with Katz’s fractal dimension of photoplethysmography

Bayesian 3D Reconstruction of Complex Scenes from Single-Photon Lidar Data

Patch-Based Sparse Representation For Bacterial Detection

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