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Mo C, Johnston R, Navarini L, Liverani FS, Ellero M. Exploring the link between coffee matrix microstructure and flow properties using combined X-ray microtomography and smoothed particle hydrodynamics simulations. Sci Rep 2023; 13:16374. [PMID: 37773195 PMCID: PMC10541431 DOI: 10.1038/s41598-023-42380-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2023] [Accepted: 09/09/2023] [Indexed: 10/01/2023] Open
Abstract
Coffee extraction involves many complex physical and transport processes extremely difficult to model. Among the many factors that will affect the final quality of coffee, the microstructure of the coffee matrix is one of the most critical ones. In this article, we use X-ray micro-computed (microCT) technique to capture the microscopic details of coffee matrices at particle-level and perform fluid dynamics simulation based on the smoothed particle hydrodynamics method (SPH) with the 3D reconstructured data. Information like flow permeability and tortuosity of the matrices can be therefore obtained from our simulation. We found that inertial effects can be quite significant at the normal pressure gradient conditions typical for espresso brewing, and can provide an explanation for the inconsistency of permeability measurements seen in the literature. Several types of coffee powder are further examined, revealing their distinct microscopic details and resulting flow features. By comparing the microCT images of pre- and post-extraction coffee matrices, it is found that a decreasing porosity profile (from the bottom-outlet to the top-inlet) always develops after extraction. This counterintuitive phenomenon can be explained using a pressure-dependent erosion model proposed in our prior work. Our results reveal not only some important hydrodynamic mechanisms of coffee extraction, but also show that microCT scan can provide useful microscopic details for coffee extraction modelling. MicroCT scan establishes the basis for a data-driven numerical framework to explore the link between coffee powder microstructure and extraction dynamics, which is the prerequisite to study the time evolution of both volatile and non-volatile organic compounds and then the flavour profile of coffee brews.
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Affiliation(s)
- Chaojie Mo
- Basque Center for Applied Mathematics (BCAM), Alameda de Mazarredo 14, 48009, Bilbao, Spain.
- Aircraft and Propulsion Laboratory, Ningbo Institute of Technology, Beihang University, Ningbo, 315100, People's Republic of China.
| | - Richard Johnston
- Faculty of Science and Engineering, Swansea University, Swansea, SA1 8EN, UK
| | | | | | - Marco Ellero
- Basque Center for Applied Mathematics (BCAM), Alameda de Mazarredo 14, 48009, Bilbao, Spain
- Zienkiewicz Centre for Computational Engineering (ZCCE), Swansea University, Bay Campus, Swansea, SA1 8EN, UK
- IKERBASQUE, Basque Foundation for Science, Calle de María Díaz de Haro 3, 48013, Bilbao, Spain
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2
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Enzlein T, Cordes J, Munteanu B, Michno W, Serneels L, De Strooper B, Hanrieder J, Wolf I, Chávez-Gutiérrez L, Hopf C. Computational Analysis of Alzheimer Amyloid Plaque Composition in 2D- and Elastically Reconstructed 3D-MALDI MS Images. Anal Chem 2020; 92:14484-14493. [PMID: 33138378 DOI: 10.1021/acs.analchem.0c02585] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
MALDI mass spectrometry imaging (MSI) enables label-free, spatially resolved analysis of a wide range of analytes in tissue sections. Quantitative analysis of MSI datasets is typically performed on single pixels or manually assigned regions of interest (ROIs). However, many sparse, small objects such as Alzheimer's disease (AD) brain deposits of amyloid peptides called plaques are neither single pixels nor ROIs. Here, we propose a new approach to facilitate the comparative computational evaluation of amyloid plaque-like objects by MSI: a fast PLAQUE PICKER tool that enables a statistical evaluation of heterogeneous amyloid peptide composition. Comparing two AD mouse models, APP NL-G-F and APP PS1, we identified distinct heterogeneous plaque populations in the NL-G-F model but only one class of plaques in the PS1 model. We propose quantitative metrics for the comparison of technical and biological MSI replicates. Furthermore, we reconstructed a high-accuracy 3D-model of amyloid plaques in a fully automated fashion, employing rigid and elastic MSI image registration using structured and plaque-unrelated reference ion images. Statistical single-plaque analysis in reconstructed 3D-MSI objects revealed the Aβ1-42Arc peptide to be located either in the core of larger plaques or in small plaques without colocalization of other Aβ isoforms. In 3D, a substantially larger number of small plaques were observed than that indicated by the 2D-MSI data, suggesting that quantitative analysis of molecularly diverse sparsely-distributed features may benefit from 3D-reconstruction. Data are available via ProteomeXchange with identifier PXD020824.
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Affiliation(s)
- Thomas Enzlein
- Center for Mass Spectrometry and Optical Spectroscopy (CeMOS), Mannheim University of Applied Sciences, Paul-Wittsack Str. 10, Mannheim 68163, Germany.,KU Leuven-VIB Center for Brain & Disease Research, VIB, Leuven 3000, Belgium.,Department of Neurosciences, Leuven Institute for Neuroscience and Disease, KU Leuven, Leuven 3000, Belgium
| | - Jonas Cordes
- Faculty of Computer Science, University of Applied Sciences Mannheim, Paul-Wittsack-Straße 10, Mannheim 68163, Germany
| | - Bogdan Munteanu
- Center for Mass Spectrometry and Optical Spectroscopy (CeMOS), Mannheim University of Applied Sciences, Paul-Wittsack Str. 10, Mannheim 68163, Germany
| | - Wojciech Michno
- Department of Psychiatry and Neurochemistry, Sahlgrenska Academy at the University of Gothenburg, Mölndal Hospital, House V3, Mölndal 43180, Sweden.,Department of Neuroscience, Physiology and Pharmacology, University College London, Gower Street, London WC1E 6BT, United Kingdom
| | - Lutgarde Serneels
- KU Leuven-VIB Center for Brain & Disease Research, VIB, Leuven 3000, Belgium.,Department of Neurosciences, Leuven Institute for Neuroscience and Disease, KU Leuven, Leuven 3000, Belgium
| | - Bart De Strooper
- KU Leuven-VIB Center for Brain & Disease Research, VIB, Leuven 3000, Belgium.,Department of Neurosciences, Leuven Institute for Neuroscience and Disease, KU Leuven, Leuven 3000, Belgium.,UK Dementia Research Institute at UCL, University College London, London WC1E 6BT U.K
| | - Jörg Hanrieder
- Department of Psychiatry and Neurochemistry, Sahlgrenska Academy at the University of Gothenburg, Mölndal Hospital, House V3, Mölndal 43180, Sweden.,Department of Neurodegenerative Diseases, University College London Queen Square Institute of Neurology, Queen Square, London WC1N 3BG, United Kingdom
| | - Ivo Wolf
- Faculty of Computer Science, University of Applied Sciences Mannheim, Paul-Wittsack-Straße 10, Mannheim 68163, Germany
| | - Lucía Chávez-Gutiérrez
- KU Leuven-VIB Center for Brain & Disease Research, VIB, Leuven 3000, Belgium.,Department of Neurosciences, Leuven Institute for Neuroscience and Disease, KU Leuven, Leuven 3000, Belgium
| | - Carsten Hopf
- Center for Mass Spectrometry and Optical Spectroscopy (CeMOS), Mannheim University of Applied Sciences, Paul-Wittsack Str. 10, Mannheim 68163, Germany
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Novel Fault Identification for Electromechanical Systems via Spectral Technique and Electrical Data Processing. ELECTRONICS 2020. [DOI: 10.3390/electronics9101560] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
It is proposed, developed, investigated, and validated by experiments and modelling for the first time in worldwide terms new data processing technologies, higher order spectral multiple correlation technologies for fault identification for electromechanical systems via electrical data processing. Investigation of the higher order spectral triple correlation technology via modelling has shown that the proposed data processing technology effectively detects component faults. The higher order spectral triple correlation technology successfully applied for rolling bearing fault identification. Experimental investigation of the technology has shown, that the technology effectively identifies rolling bearing fault by electrical data processing at very early stage of fault development. Novel technology comparisons via modelling and experiments of the proposed higher order spectral triple correlation technology and the higher order spectra technology show the higher fault identification effectiveness of the proposed technology over the bicoherence technology.
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Michalak H, Okarma K. Robust Combined Binarization Method of Non-Uniformly Illuminated Document Images for Alphanumerical Character Recognition. SENSORS (BASEL, SWITZERLAND) 2020; 20:E2914. [PMID: 32455623 PMCID: PMC7287981 DOI: 10.3390/s20102914] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/30/2020] [Revised: 05/13/2020] [Accepted: 05/19/2020] [Indexed: 11/16/2022]
Abstract
Image binarization is one of the key operations decreasing the amount of information used in further analysis of image data, significantly influencing the final results. Although in some applications, where well illuminated images may be easily captured, ensuring a high contrast, even a simple global thresholding may be sufficient, there are some more challenging solutions, e.g., based on the analysis of natural images or assuming the presence of some quality degradations, such as in historical document images. Considering the variety of image binarization methods, as well as their different applications and types of images, one cannot expect a single universal thresholding method that would be the best solution for all images. Nevertheless, since one of the most common operations preceded by the binarization is the Optical Character Recognition (OCR), which may also be applied for non-uniformly illuminated images captured by camera sensors mounted in mobile phones, the development of even better binarization methods in view of the maximization of the OCR accuracy is still expected. Therefore, in this paper, the idea of the use of robust combined measures is presented, making it possible to bring together the advantages of various methods, including some recently proposed approaches based on entropy filtering and a multi-layered stack of regions. The experimental results, obtained for a dataset of 176 non-uniformly illuminated document images, referred to as the WEZUT OCR Dataset, confirm the validity and usefulness of the proposed approach, leading to a significant increase of the recognition accuracy.
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Affiliation(s)
| | - Krzysztof Okarma
- Faculty of Electrical Engineering, West Pomeranian University of Technology in Szczecin, 70-313 Szczecin, Poland;
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Hsu JT, Chen YW, Ho TW, Tai HC, Wu JM, Sun HY, Hung CS, Zeng YC, Kuo SY, Lai F. Chronic wound assessment and infection detection method. BMC Med Inform Decis Mak 2019; 19:99. [PMID: 31126274 PMCID: PMC6534841 DOI: 10.1186/s12911-019-0813-0] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2017] [Accepted: 04/09/2019] [Indexed: 11/11/2022] Open
Abstract
Background Numerous patients suffer from chronic wounds and wound infections nowadays. Until now, the care for wounds after surgery still remain a tedious and challenging work for the medical personnel and patients. As a result, with the help of the hand-held mobile devices, there is high demand for the development of a series of algorithms and related methods for wound infection early detection and wound self monitoring. Methods This research proposed an automated way to perform (1) wound image segmentation and (2) wound infection assessment after surgical operations. The first part describes an edge-based self-adaptive threshold detection image segmentation method to exclude nonwounded areas from the original images. The second part describes a wound infection assessment method based on machine learning approach. In this method, the extraction of feature points from the suture area and an optimal clustering method based on unimodal Rosin threshold algorithm that divides feature points into clusters are introduced. These clusters are then merged into several regions of interest (ROIs), each of which is regarded as a suture site. Notably, a support vector machine (SVM) can automatically interpret infections on these detected suture site. Results For (1) wound image segmentation, boundary-based evaluation were applied on 100 images with gold standard set up by three physicians. Overall, it achieves 76.44% true positive rate and 89.04% accuracy value. For (2) wound infection assessment, the results from a retrospective study using confirmed wound pictures from three physicians for the following four symptoms are presented: (1) Swelling, (2) Granulation, (3) Infection, and (4) Tissue Necrosis. Through cross-validation of 134 wound images, for anomaly detection, our classifiers achieved 87.31% accuracy value; for symptom assessment, our classifiers achieved 83.58% accuracy value. Conclusions This augmentation mechanism has been demonstrated reliable enough to reduce the need for face-to-face diagnoses. To facilitate the use of this method and analytical framework, an automatic wound interpretation app and an accompanying website were developed. Trial registration 201505164RIND, 201803108RSB.
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Affiliation(s)
- Jui-Tse Hsu
- Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Room 410, Barry Lam Hall, No.1, Sec.4, Roosevelt Road, Taipei, 10617, Taiwan, Republic of China.
| | - Yung-Wei Chen
- Department of Electrical Engineering, National Taiwan University, No.1, Sec. 4, Roosevelt Road, Taipei, 10617, Taiwan, Republic of China
| | - Te-Wei Ho
- Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Room 410, Barry Lam Hall, No.1, Sec.4, Roosevelt Road, Taipei, 10617, Taiwan, Republic of China
| | - Hao-Chih Tai
- Department of Surgery, National Taiwan University Hospital, No.1, Changde St., Zhongzheng Dist., Taipei, 10048, Taiwan, Republic of China
| | - Jin-Ming Wu
- Department of Surgery, National Taiwan University Hospital, No.1, Changde St., Zhongzheng Dist., Taipei, 10048, Taiwan, Republic of China
| | - Hsin-Yun Sun
- Department of Internal Medicine, National Taiwan University Hospital, No.1, Changde St., Zhongzheng Dist., Taipei, 10048, Taiwan, Republic of China
| | - Chi-Sheng Hung
- Department of Internal Medicine, National Taiwan University Hospital, No.1, Changde St., Zhongzheng Dist., Taipei, 10048, Taiwan, Republic of China
| | - Yi-Chong Zeng
- Data Analytics Technology and Applications Research Institute, Institute for Information Industry, 11F, No. 106, Sec. 2, Heping E. Rd., Taipei, 106, Taiwan, Republic of China
| | - Sy-Yen Kuo
- Department of Electrical Engineering, National Taiwan University, No.1, Sec. 4, Roosevelt Road, Taipei, 10617, Taiwan, Republic of China
| | - Feipei Lai
- Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Room 410, Barry Lam Hall, No.1, Sec.4, Roosevelt Road, Taipei, 10617, Taiwan, Republic of China
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6
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Korfiatis VC, Tassani S, Matsopoulos GK. An Independent Active Contours Segmentation framework for bone micro-CT images. Comput Biol Med 2017. [PMID: 28651071 DOI: 10.1016/j.compbiomed.2017.06.016] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Micro-CT is an imaging technique for small tissues and objects that is gaining increased popularity especially as a pre-clinical application. Nevertheless, there is no well-established micro-CT segmentation method, while typical procedures lack sophistication and frequently require a degree of manual intervention, leading to errors and subjective results. To address these issues, a novel segmentation framework, called Independent Active Contours Segmentation (IACS), is proposed in this paper. The proposed IACS is based on two autonomous modules, namely automatic ROI extraction and IAC Evolution, which segments the ROI image using multiple Active Contours that evolve simultaneously and independently of one another. The proposed method is applied on a Phantom dataset and on real datasets. It is tested against several established segmentation methods that include Adaptive Thresholding, Otsu Thresholding, Region Growing, Chan-Vese (CV) AC, Geodesic AC and Automatic Local Ratio-CV AC, both qualitatively and quantitatively. The results prove its superior performance in terms of object identification capability, accuracy and robustness, under normal circumstances and under four types of artificially introduced noise. These enhancements can lead to more reliable analysis, better diagnostic procedures and treatment evaluation of several bone-related pathologies, and to the facilitation and further advancement of bone research.
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Affiliation(s)
- Vasileios Ch Korfiatis
- School of Electrical and Computer Engineering, National Technical University of Athens, Greece
| | - Simone Tassani
- Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain
| | - George K Matsopoulos
- School of Electrical and Computer Engineering, National Technical University of Athens, Greece.
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7
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Zou Y, Lei B, Dong F, Xu G, Sun S, Xia P. Structure similarity-guided image binarization for automatic segmentation of epidermis surface microstructure images. J Microsc 2017; 266:153-165. [PMID: 28117893 DOI: 10.1111/jmi.12525] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2015] [Revised: 12/11/2016] [Accepted: 01/01/2017] [Indexed: 11/28/2022]
Abstract
Partitioning epidermis surface microstructure (ESM) images into skin ridge and skin furrow regions is an important preprocessing step before quantitative analyses on ESM images. Binarization segmentation is a potential technique for partitioning ESM images because of its computational simplicity and ease of implementation. However, even for some state-of-the-art binarization methods, it remains a challenge to automatically segment ESM images, because the grey-level histograms of ESM images have no obvious external features to guide automatic assessment of appropriate thresholds. Inspired by human visual perceptual functions of structural feature extraction and comparison, we propose a structure similarity-guided image binarization method. The proposed method seeks for the binary image that best approximates the input ESM image in terms of structural features. The proposed method is validated by comparing it with two recently developed automatic binarization techniques as well as a manual binarization method on 20 synthetic noisy images and 30 ESM images. The experimental results show: (1) the proposed method possesses self-adaption ability to cope with different images with same grey-level histogram; (2) compared to two automatic binarization techniques, the proposed method significantly improves average accuracy in segmenting ESM images with an acceptable decrease in computational efficiency; (3) and the proposed method is applicable for segmenting practical EMS images. (Matlab code of the proposed method can be obtained by contacting with the corresponding author.).
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Affiliation(s)
- Y Zou
- Institute of Intelligent Vision and Image Information, China Three Gorges University, Hubei, China.,Group for Biomedical Imaging and Bioinformatics, China Three Gorges University, Hubei, China
| | - B Lei
- Centre for Microscopy Analysis, China Three Gorges University, Hubei, China.,Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering, China Three Gorges University, Hubei, China
| | - F Dong
- Institute of Intelligent Vision and Image Information, China Three Gorges University, Hubei, China
| | - G Xu
- Institute of Intelligent Vision and Image Information, China Three Gorges University, Hubei, China.,Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering, China Three Gorges University, Hubei, China
| | - S Sun
- Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering, China Three Gorges University, Hubei, China
| | - P Xia
- Centre for Microscopy Analysis, China Three Gorges University, Hubei, China.,Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering, China Three Gorges University, Hubei, China
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8
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Cao F, Cai M, Chu J, Zhao J, Zhou Z. A novel segmentation algorithm for nucleus in white blood cells based on low-rank representation. Neural Comput Appl 2016. [DOI: 10.1007/s00521-016-2391-8] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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9
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Terletzky P, Ramsey RD. A semi-automated single day image differencing technique to identify animals in aerial imagery. PLoS One 2014; 9:e85239. [PMID: 24454827 PMCID: PMC3891695 DOI: 10.1371/journal.pone.0085239] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2013] [Accepted: 11/25/2013] [Indexed: 11/23/2022] Open
Abstract
Our research presents a proof-of-concept that explores a new and innovative method to identify large animals in aerial imagery with single day image differencing. We acquired two aerial images of eight fenced pastures and conducted a principal component analysis of each image. We then subtracted the first principal component of the two pasture images followed by heuristic thresholding to generate polygons. The number of polygons represented the number of potential cattle (Bos taurus) and horses (Equus caballus) in the pasture. The process was considered semi-automated because we were not able to automate the identification of spatial or spectral thresholding values. Imagery was acquired concurrently with ground counts of animal numbers. Across the eight pastures, 82% of the animals were correctly identified, mean percent commission was 53%, and mean percent omission was 18%. The high commission error was due to small mis-alignments generated from image-to-image registration, misidentified shadows, and grouping behavior of animals. The high probability of correctly identifying animals suggests short time interval image differencing could provide a new technique to enumerate wild ungulates occupying grassland ecosystems, especially in isolated or difficult to access areas. To our knowledge, this was the first attempt to use standard change detection techniques to identify and enumerate large ungulates.
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Affiliation(s)
- Pat Terletzky
- Department of Wildland Resources, Utah State University, Logan, Utah, United States of America
- * E-mail:
| | - Robert Douglas Ramsey
- Department of Wildland Resources, Utah State University, Logan, Utah, United States of America
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Ghaye J, Kamat MA, Corbino-Giunta L, Silacci P, Vergères G, De Micheli G, Carrara S. Image thresholding techniques for localization of sub-resolution fluorescent biomarkers. Cytometry A 2013; 83:1001-16. [PMID: 24105983 DOI: 10.1002/cyto.a.22345] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2012] [Revised: 04/29/2013] [Accepted: 07/16/2013] [Indexed: 11/09/2022]
Abstract
In this article, we explore adaptive global and local segmentation techniques for a lab-on-chip nutrition monitoring system (NutriChip). The experimental setup consists of Caco-2 intestinal cells that can be artificially stimulated to trigger an immune response. The eventual response is optically monitored using immunofluoresence techniques targeting toll-like receptor 2 (TLR2). Two problems of interest need to be addressed by means of image processing. First, a new cell sample must be properly classified as stimulated or not. Second, the location of the stained TLR2 must be recovered in case the sample has been stimulated. The algorithmic approach to solving these problems is based on the ability of a segmentation technique to properly segment fluorescent spots. The sample classification is based on the amount and intensity of the segmented pixels, while the various segmenting blobs provide an approximate localization of TLR2. A novel local thresholding algorithm and three well-known spot segmentation techniques are compared in this study. Quantitative assessment of these techniques based on real and synthesized data demonstrates the improved segmentation capabilities of the proposed algorithm.
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Affiliation(s)
- Julien Ghaye
- Laboratory of Integrated Systems (LSI), Swiss Federal Institute of Technology, EPFL, Lausanne, Switzerland
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Daniel Lam B, Anthony EC, Hordijk PL. Analysis of nucleo-cytoplasmic shuttling of the proto-oncogene SET/I2PP2A. Cytometry A 2011; 81:81-9. [DOI: 10.1002/cyto.a.21153] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2011] [Revised: 07/26/2011] [Accepted: 09/16/2011] [Indexed: 02/03/2023]
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13
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Macfarlane C, Ogden GN. Automated estimation of foliage cover in forest understorey from digital nadir images. Methods Ecol Evol 2011. [DOI: 10.1111/j.2041-210x.2011.00151.x] [Citation(s) in RCA: 51] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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14
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