Persistent homology of fractional Gaussian noise

Masoomy, Hosein; Askari, B.; Najafi, M. N.; Movahed, M. Sadegh

doi:10.1103/physreve.104.034116

Cited by 8 publications

(3 citation statements)

References 63 publications

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“…Computationally, the primary segmentation's runtime is on-par with existing machine learning methods, but preprocessing and boundary identification often take longer [6,9]. While TOBLERONE can function with only a single parameter, it is not always obvious how to appropriately select its value-however, topological features in images are typically robust to variations in intensity, so a range of persistence thresholds will usually return a suitable segmentation [29][30][31]. As a topological image analysis technique, TOBLERONE is invariant of cell and organelle morphology [32,33].…”

Section: Discussionmentioning

confidence: 99%

Topology‐based fluorescence image analysis for automated cell identification and segmentation

Panconi

Makarova

Lambert

et al. 2022

Journal of Biophotonics

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Cell segmentation refers to the body of techniques used to identify cells in images and extract biologically relevant information from them; however, manual segmentation is laborious and subjective. We present Topological Boundary Line Estimation using Recurrence Of Neighbouring Emissions (TOBLERONE), a topological image analysis tool which identifies persistent homological image features as opposed to the geometric analysis commonly employed. We demonstrate that topological data analysis can provide accurate segmentation of arbitrarily‐shaped cells, offering a means for automatic and objective data extraction. One cellular feature of particular interest in biology is the plasma membrane, which has been shown to present varying degrees of lipid packing, or membrane order, depending on the function and morphology of the cell type. With the use of environmentally‐sensitive dyes, images derived from confocal microscopy can be used to quantify the degree of membrane order. We demonstrate that TOBLERONE is capable of automating this task.

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Section: Discussionmentioning

confidence: 99%

Topology‐based fluorescence image analysis for automated cell identification and segmentation

Panconi

Makarova

Lambert

et al. 2022

Journal of Biophotonics

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“…This can disrupt the dynamics of quantum systems, necessitating further investigation. Because of its larger timescale correlations, F G n has been utilized to analyze meteorological information [58], traffic control analysis [59], and electrical measurements [60]. The OU n has been extensively studied with the dynamics of quantum systems, as described in [57].…”

Section: Impact Of Local Gaussian Noisesmentioning

confidence: 99%

Decoherence Effects in a Three-Level System under Gaussian Process

Zangi

Rahman

et al. 2022

Symmetry

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When subjected to a classical fluctuating field characterized by a Gaussian process, we examine the purity and coherence protection in a three-level quantum system. This symmetry of the three-level system is examined when the local random field is investigated further in the noiseless and noisy regimes. In particular, we consider fractional Gaussian, Gaussian, Ornstein–Uhlenbeck, and power law noisy regimes. We show that the destructive nature of the Ornstein–Uhlenbeck noise toward the symmetry of the qutrit to preserve encoded purity and coherence remains large. Our findings suggest that properly adjusting the noisy parameters to specifically provided values can facilitate optimal extended purity and coherence survival. Non-vanishing terms appear in the final density matrix of the single qutrit system, indicating that it is in a strong coherence regime. Because of all of the Gaussian noises, monotonic decay with no revivals has been observed in the single qutrit system. In terms of coherence and information preservation, we find that the current qutrit system outperforms systems with multiple qubits or qutrits using purity and von Neumann entropy. A comparison of noisy and noiseless situations shows that the fluctuating nature of the local random fields is ultimately lost when influenced using the classical Gaussian noises.

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“…Computationally, the primary segmentation's runtime is on-par with existing machine learning methods, but preprocessing and boundary identification often take longer [6,9]. While TOBLERONE can function with only a single parameter, it is not always obvious how to appropriately select its value -however, topological features in images are typically robust to variations in intensity, so a range of persistence thresholds will usually return a suitable segmentation [26][27][28]. As a topological image analysis technique, TOBLERONE is invariant of cell and organelle morphology [29,30].…”

Section: Demonstration With Experimental Datamentioning

confidence: 99%

Topology-based fluorescence image analysis for automated cell identification and segmentation

Panconi

Makarova

Lambert

et al. 2022

Preprint

View full text Add to dashboard Cite

Cell segmentation refers to the body of techniques used to identify cells in images and extract biologically relevant information from them; however, manual segmentation is laborious and subjective. We present Topological Boundary Line Estimation using Recurrence Of Neighbouring Emissions (TOBLERONE), a topological image analysis tool which identifies persistent homological image features as opposed to the geometric analysis commonly employed. We demonstrate that topological data analysis can provide accurate segmentation of arbitrarily-shaped cells, offering a means for automatic and objective data extraction. One cellular feature of particular interest in biology is the plasma membrane, which has been shown to present varying degrees of lipid packing, or membrane order, depending on the function and morphology of the cell type. With the use of environmentally-sensitive dyes, images derived from confocal microscopy can be used to quantify the degree of membrane order. We demonstrate that TOBLERONE is capable of automating this task.

show abstract

Persistent homology of fractional Gaussian noise

Cited by 8 publications

References 63 publications

Topology‐based fluorescence image analysis for automated cell identification and segmentation

Topology‐based fluorescence image analysis for automated cell identification and segmentation

Decoherence Effects in a Three-Level System under Gaussian Process

Topology-based fluorescence image analysis for automated cell identification and segmentation

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