scholarly journals MF2C3: Multi-Feature Fuzzy Clustering to Enhance Cell Colony Detection in Automated Clonogenic Assay Evaluation

Symmetry ◽  
2020 ◽  
Vol 12 (5) ◽  
pp. 773 ◽  
Author(s):  
Carmelo Militello ◽  
Leonardo Rundo ◽  
Luigi Minafra ◽  
Francesco Paolo Cammarata ◽  
Marco Calvaruso ◽  
...  
Keyword(s):  
Clonogenic Assay ◽  
Ground Truth ◽  
General Purpose ◽  
Computational Method ◽  
Counting Procedure ◽  
Proliferative Effect ◽  
Biological Studies ◽  
Biological Technique ◽  
Fuzzy C Means Clustering ◽  
Cell Colony

A clonogenic assay is a biological technique for calculating the Surviving Fraction (SF) that quantifies the anti-proliferative effect of treatments on cell cultures: this evaluation is often performed via manual counting of cell colony-forming units. Unfortunately, this procedure is error-prone and strongly affected by operator dependence. Besides, conventional assessment does not deal with the colony size, which is generally correlated with the delivered radiation dose or administered cytotoxic agent. Relying upon the direct proportional relationship between the Area Covered by Colony (ACC) and the colony count and size, along with the growth rate, we propose MF2C3, a novel computational method leveraging spatial Fuzzy C-Means clustering on multiple local features (i.e., entropy and standard deviation extracted from the input color images acquired by a general-purpose flat-bed scanner) for ACC-based SF quantification, by considering only the covering percentage. To evaluate the accuracy of the proposed fully automatic approach, we compared the SFs obtained by MF2C3 against the conventional counting procedure on four different cell lines. The achieved results revealed a high correlation with the ground-truth measurements based on colony counting, by outperforming our previously validated method using local thresholding on L*u*v* color well images. In conclusion, the proposed multi-feature approach, which inherently leverages the concept of symmetry in the pixel local distributions, might be reliably used in biological studies.

Area-based cell colony surviving fraction evaluation: A novel fully automatic approach using general-purpose acquisition hardware

2017 ◽  
Vol 89 ◽  
pp. 454-465 ◽  
Author(s):  
Carmelo Militello ◽  
Leonardo Rundo ◽  
Vincenzo Conti ◽  
Luigi Minafra ◽  
Francesco Paolo Cammarata ◽  
...  
Keyword(s):  
General Purpose ◽  
Fully Automatic ◽  
Cell Colony

scholarly journals Entity deduplication in big data graphs for scholarly communication

2020 ◽  
Vol 54 (4) ◽  
pp. 409-435
Author(s):  
Paolo Manghi ◽  
Claudio Atzori ◽  
Michele De Bonis ◽  
Alessia Bardi
Keyword(s):  
Big Data ◽  
Ground Truth ◽  
Open Science ◽  
General Purpose ◽  
High Level Architecture ◽  
Multiple Sources ◽  
Actual Problem ◽  
Content Type ◽  
Infrastructure System ◽  
High Level

PurposeSeveral online services offer functionalities to access information from “big research graphs” (e.g. Google Scholar, OpenAIRE, Microsoft Academic Graph), which correlate scholarly/scientific communication entities such as publications, authors, datasets, organizations, projects, funders, etc. Depending on the target users, access can vary from search and browse content to the consumption of statistics for monitoring and provision of feedback. Such graphs are populated over time as aggregations of multiple sources and therefore suffer from major entity-duplication problems. Although deduplication of graphs is a known and actual problem, existing solutions are dedicated to specific scenarios, operate on flat collections, local topology-drive challenges and cannot therefore be re-used in other contexts.Design/methodology/approachThis work presents GDup, an integrated, scalable, general-purpose system that can be customized to address deduplication over arbitrary large information graphs. The paper presents its high-level architecture, its implementation as a service used within the OpenAIRE infrastructure system and reports numbers of real-case experiments.FindingsGDup provides the functionalities required to deliver a fully-fledged entity deduplication workflow over a generic input graph. The system offers out-of-the-box Ground Truth management, acquisition of feedback from data curators and algorithms for identifying and merging duplicates, to obtain an output disambiguated graph.Originality/valueTo our knowledge GDup is the only system in the literature that offers an integrated and general-purpose solution for the deduplication graphs, while targeting big data scalability issues. GDup is today one of the key modules of the OpenAIRE infrastructure production system, which monitors Open Science trends on behalf of the European Commission, National funders and institutions.

Numerical Implementation of a Multi-Physical Fields Coupling Constitute Model

2013 ◽  
Vol 457-458 ◽  
pp. 354-357
Author(s):  
Yu Jie Sun ◽  
Qing Chun Cui ◽  
Suo Huai Zhang ◽  
Li Jun Yan
Keyword(s):  
Material Properties ◽  
General Purpose ◽  
Computational Method ◽  
Numerical Implementation ◽  
Temperature Dependent ◽  
Working Process ◽  
Finite Element Program ◽  
Element Program ◽  
User Material Subroutine ◽  
Constitute Model

The objective of this paper provides a numerical implementation procedure of thermo-metallurgical-mechanical constitute equation based on additively decomposition of strain rate. Together with phase transformation kinetics, the macro material properties are determined by assigning temperature dependent material properties to each phase and by applying mixture rule to combine. Then the constitute equation is implemented into general purpose implicit finite element program via user material subroutine. The effectiveness of developed computational method is confirmed by a Satoh test simulation. Simulation of Satoh test demonstrates that transformation induce plasticity has significant effect of the evolution of residual stress and can not be neglected for alloy steel during hot working process.

scholarly journals Quantitative Measurement of Breast Tumors Using Intravoxel Incoherent Motion (IVIM) MR Images

2021 ◽  
Vol 11 (7) ◽  
pp. 656
Author(s):  
Si-Wa Chan ◽  
Wei-Hsuan Hu ◽  
Yen-Chieh Ouyang ◽  
Hsien-Chi Su ◽  
Chin-Yao Lin ◽  
...  
Keyword(s):  
Quantitative Measurement ◽  
Hyperspectral Image ◽  
Clustering Algorithms ◽  
Ground Truth ◽  
Breast Tumors ◽  
Tumor Detection ◽  
Intravoxel Incoherent Motion ◽  
Detection Methods ◽  
Clustering Methods ◽  
Fuzzy C Means Clustering

Breast magnetic resonance imaging (MRI) is currently a widely used clinical examination tool. Recently, MR diffusion-related technologies, such as intravoxel incoherent motion diffusion weighted imaging (IVIM-DWI), have been extensively studied by breast cancer researchers and gradually adopted in clinical practice. In this study, we explored automatic tumor detection by IVIM-DWI. We considered the acquired IVIM-DWI data as a hyperspectral image cube and used a well-known hyperspectral subpixel target detection technique: constrained energy minimization (CEM). Two extended CEM methods—kernel CEM (K-CEM) and iterative CEM (I-CEM)—were employed to detect breast tumors. The K-means and fuzzy C-means clustering algorithms were also evaluated. The quantitative measurement results were compared to dynamic contrast-enhanced T1-MR imaging as ground truth. All four methods were successful in detecting tumors for all the patients studied. The clustering methods were found to be faster, but the CEM methods demonstrated better performance according to both the Dice and Jaccard metrics. These unsupervised tumor detection methods have the advantage of potentially eliminating operator variability. The quantitative results can be measured by using ADC, signal attenuation slope, D*, D, and PF parameters to classify tumors of mass, non-mass, cyst, and fibroadenoma types.

scholarly journals Performance Evaluation of Change Detection in SAR Images Based on Hybrid Antlion DWT Fuzzy c-Means Clustering

2021 ◽  
Vol 21 (2) ◽  
pp. 45-57
Author(s):  
J. Thrisul Kumar ◽  
B. M. S. Rani ◽  
M. Satish Kumar ◽  
M. V. Raju ◽  
K. Maria Das
Keyword(s):  
Change Detection ◽  
Image Fusion ◽  
Geographical Area ◽  
Ground Truth ◽  
Sar Images ◽  
Fuzzy C Means ◽  
Difference Image ◽  
Multi Temporal ◽  
Fuzzy C Means Clustering ◽  
Log Ratio

Abstract In this paper, the main objective is to detect changes in the geographical area of Ottawa city in Canada due to floods. Two multi-temporal Synthetic Aperture Radar (SAR) images have been taken to evaluate the un-supervised change detection process. In this process, two ratio operators named as Log-Ratio and Mean-Ratio are used to generate a difference image. Performing image fusion based on DWT by selecting optimum filter coefficients by satisfying the wavelet filter coefficient properties through a novel image fusion technique is named as ADWT. GA, PSO, AntLion Optimization algorithms (ALO) and Hybridized AntLion Algorithm (HALO) have been adapted to perform the ADWT based image fusion. Segmentation has been performed based on fuzzy c-Means clustering to detect changed and unchanged pixels. Finally, the performance of the proposed method will be analysed by comparing the segmented image with the ground truth image in terms of sensitivity, accuracy, specificity, precision, F1-score.

scholarly journals Leaf Segmentation of Rosette Plants using Rough K-Means in CIELab Color Space

2021 ◽  
Author(s):  
Arunita Das ◽  
Daipayan Ghosal ◽  
Krishna Gopal Dhal
Keyword(s):  
Clustering Algorithms ◽  
Color Space ◽  
Ground Truth ◽  
Accurate Analysis ◽  
Partitional Clustering ◽  
Modern Agriculture ◽  
Cielab Color Space ◽  
Fuzzy C Means Clustering ◽  
Leaf Segmentation ◽  
Cielab Color

Segmentation of Plant Images plays an important role in modern agriculture where it can provide accurate analysis of a plant’s growth and possi-ble anomalies. In this paper, rough set based partitional clustering technique called Rough K-Means has been utilized in CIELab color space for the proper leaf segmentation of rosette plants. The eÿcacy of the proposed technique have been analysed by comparing it with the results of tra-ditional K-Means and Fuzzy C-Means clustering algorithms. The visual and numerical results re-veal that the RKM in CIELab provides the near-est result to the ideal ground truth, hence the most eÿcient one.

scholarly journals A robust algorithm for optic disc segmentation and fovea detection in retinal fundus images

2017 ◽  
Vol 3 (2) ◽  
pp. 533-537 ◽  
Author(s):  
Caterina Rust ◽  
Stephanie Häger ◽  
Nadine Traulsen ◽  
Jan Modersitzki
Keyword(s):  
Success Rate ◽  
Optic Disc ◽  
Ground Truth ◽  
Morphological Operations ◽  
Fundus Images ◽  
Iterative Thresholding ◽  
Fuzzy C Means Clustering ◽  
Multiple Regions ◽  
Retinal Fundus Images ◽  
Retinal Fundus

AbstractAccurate optic disc (OD) segmentation and fovea detection in retinal fundus images are crucial for diagnosis in ophthalmology. We propose a robust and broadly applicable algorithm for automated, robust, reliable and consistent fovea detection based on OD segmentation. The OD segmentation is performed with morphological operations and Fuzzy C Means Clustering combined with iterative thresholding on a foreground segmentation. The fovea detection is based on a vessel segmentation via morphological operations and uses the resulting OD segmentation to determine multiple regions of interest. The fovea is determined from the largest, vessel-free candidate region. We have tested the novel method on a total of 190 images from three publicly available databases DRIONS, Drive and HRF. Compared to results of two human experts for DRIONS database, our OD segmentation yielded a dice coefficient of 0.83. Note that missing ground truth and expert variability is an issue. The new scheme achieved an overall success rate of 99.44% for OD detection and an overall success rate of 96.25% for fovea detection, which is superior to state-of-the-art approaches.

Recursive Linearization of Multibody Dynamics and Application to Control Design

1994 ◽  
Vol 116 (2) ◽  
pp. 445-451 ◽  
Author(s):  
Tsung-Chieh Lin ◽  
K. Harold Yae
Keyword(s):  
Multibody Dynamics ◽  
Equations Of Motion ◽  
Control Design ◽  
Numerical Differentiation ◽  
Difficult Problem ◽  
General Purpose ◽  
Computational Method ◽  
Computational Error ◽  
Dynamics Modeling ◽  
Analytical Approximations

The nonlinear equations of motion in multibody dynamics pose a difficult problem in linear control design. It is therefore desirable to have linearization capability in conjunction with a general-purpose multibody dynamics modeling technique. A new computational method for linearization is obtained by applying a series of first-order analytical approximations to the recursive kinematic relationships. The method has proved to be computationally more efficient. It has also turned out to be more accurate because the analytical perturbation requires matrix and vector operations by circumventing numerical differentiation and other associated numerical operations that may accumulate computational error.

scholarly journals iCOBRA: open, reproducible, standardized and live method benchmarking

2015 ◽  
Author(s):  
Charlotte Soneson ◽  
Mark D Robinson
Keyword(s):  
Ground Truth ◽  
R Package ◽  
General Purpose ◽  
Web Based ◽  
Standard Format ◽  
Standardized Method

We present iCOBRA, a flexible general-purpose web-based application and accompanying R package to evaluate, compare and visualize the performance of methods for estimation or classification when ground truth is available. iCOBRA is interactive, can be run locally or remotely and generates customizable, publication-ready graphics. To facilitate open, reproducible and standardized method comparisons, expanding as new innovations are made, we encourage the community to provide benchmark results in a standard format.

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