Live Cell Segmentation in Fluorescence Microscopy via Graph Cut

Lesko, Milan; Kató, Zoltán; Nagy, Antal; Gombos, Imre; Török, Zsolt; Vı́gh, László

doi:10.1109/icpr.2010.367

Cited by 16 publications

(7 citation statements)

References 14 publications

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“…Clustering based, histogram based are the generally known thresholding methods [11][12][13]. More progressive methods include graph cut and level set based, but these lead to more computational complexity [12,14,15]. Here a basic thresholding method was applied where pixels in input image having luminance value greater than threshold value are replaced with value 1, while rest of the pixels is replaced with value 0.…”

Section: Binary Conversionmentioning

confidence: 99%

Robust Automated detection of Nanocarriers’ Toxicity using Microscopic Image Analysis

Saini

Srivastava

Bajpai³

2018

ICST Transactions on Scalable Information Systems

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Nanocarriers' usage turned out to be a revolutionizing factor in the field of medical diagnosis and therapies. One of these therapies includes drug delivery system, where the nanocarriers are utilized for targeted and controlled delivery of drug to the diseased sites. Toxicity is one of the crucial issues which can arise during this process and needs to be addressed seriously and as early as possible. This paper reports an automated system for the relatively new and undiscovered area, where cell detection and evaluation play a major role in toxicity prediction of nanocarriers during the drug delivery process. The toxicity level was decided on the basis of dead cells count present in the microscopic images. The algorithm takes a few seconds to run and the overall accuracy of the proposed algorithm was found to be approx. 97% and 83% for different sets of images. The various image peculiarities which led to error include high cells clustering, poor contrast, and noisy background.

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Section: Binary Conversionmentioning

confidence: 99%

Robust Automated detection of Nanocarriers’ Toxicity using Microscopic Image Analysis

Saini

Srivastava

Bajpai³

2018

ICST Transactions on Scalable Information Systems

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“…Thresholding techniques are used predominantly for single molecule and cell segmentation [4,8]. Otsu's method [9] is still common, however more suitable algorithms exist in terms of typical intensity distributions such as valley-emphasis [10,11] and graph cut [11].…”

Section: Introductionmentioning

confidence: 99%

A novel flash detection algorithm for single molecule counting with TIRF microscopy

Radford

Wang

et al. 2013

2013 IEEE 10th International Symposium on Biomedical Imaging

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A novel algorithm, Adjacent Pixel Temporal Intensity Correlation (APTIC), was developed to detect single fluorescent molecules by their stochastic emission patterns; photoblinking and photobleaching. The algorithm was evaluated using simulated image data and Total Internal Reflection Fluorescence Microscopy (TIRF-M) to count the number fluorescently labelled protein molecules adsorbed onto glass substrates modified by Radio Frequency Glow Discharge (RFGD) deposition. By selecting an appropriate correlation threshold, the algorithm was capable of detecting synthetic flashes with a signal-to-noise ratio (SNR) as low as 2.0 with 90% sensitivity. The methodology holds great promise for mapping the amount and distribution of biomolecules on surfaces.

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“…Graph cutting is gaining traction for feature segmentation in a few biological imaging applications. In general, its utility has been demonstrated in applications involving segmentation of predefined shapes, like ellipses, for features with good contrast against the backgrounds, such as cells, chromosomes, and protein segmentation and identification (Leskó et al, 2010; Beheshti et al, 2015; Soubies et al, 2015), as well as analysis of bone tissue images to quantify the blood permeability of bone marrow (Shigeta et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

Application of the Maximum Flow–Minimum Cut Algorithm to Segmentation and Clustering of Materials Datasets

et al. 2019

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Problems involving image segmentation, atomic cluster identification, segmentation of microstructure constituents in images and austenite reconstruction have seen various approaches attempt to solve them with mixed results. No single computational technique has been able to effectively tackle these problems due to the vast differences between them. We propose the application of graph cutting as a versatile technique that can provide solutions to numerous materials data analysis problems. This can be attributed to its configuration flexibility coupled with the ability to handle noisy experimental data. Implementation of a Bayesian statistical approach allows for the prior information, based on experimental results and already ingrained within nodes, to drive the expected solutions. This way, nodes within the graph can be grouped together with similar, neighboring nodes that are then assigned to a specific system with respect to calculated likelihoods. Associating probabilities with potential solutions and states of the system allows for quantitative, stochastic analysis. The promising, robust results for each problem indicate the potential usefulness of the technique so long as a network of nodes can be effectively established within the model system.

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Live Cell Segmentation in Fluorescence Microscopy via Graph Cut

Cited by 16 publications

References 14 publications

Robust Automated detection of Nanocarriers’ Toxicity using Microscopic Image Analysis

Robust Automated detection of Nanocarriers’ Toxicity using Microscopic Image Analysis

A novel flash detection algorithm for single molecule counting with TIRF microscopy

Application of the Maximum Flow–Minimum Cut Algorithm to Segmentation and Clustering of Materials Datasets

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