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Luengo Hendriks, Cris L.
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Publications (10 of 47) Show all publications
Joffre, T., Segerholm, K., Persson, C., Bardage, S. L., Luengo Hendriks, C. L. & Isaksson, P. (2017). Characterization of interfacial stress transfer ability in acetylation-treated wood fibre composites using X-ray microtomography. Industrial crops and products (Print), 95, 43-49
Open this publication in new window or tab >>Characterization of interfacial stress transfer ability in acetylation-treated wood fibre composites using X-ray microtomography
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2017 (English)In: Industrial crops and products (Print), ISSN 0926-6690, E-ISSN 1872-633X, Vol. 95, p. 43-49Article in journal (Refereed) Published
Abstract [en]

The properties of the fibre/matrix interface contribute to stiffness, strength and fracture behaviour of fibre-reinforced composites. In cellulosic composites, the limited affinity between the hydrophilic fibres and the hydrophobic thermoplastic matrix remains a challenge, and the reinforcing capability ofthe fibres is hence not fully utilized. A direct characterisation of the stress transfer ability through pull-out tests on single fibres is extremely cumbersome due to the small dimension of the wood fibres. Here a novel approach is proposed:the length distribution ofthe fibres sticking out ofthe matrix atthe fracture surface is approximated using X-ray microtomography and is used as an estimate of the adhesion between the fibres and the matrix. When a crack grows in the material, the fibres will either break or be pulled-out of the matrix depending on their adhesion to the matrix: good adhesion between the fibres and the matrix should result in more fibre breakage and less pull-out of the fibres than poor adhesion. The effect of acetylation on the adhesion between the wood fibres and the PLA matrix was evaluated at different moisture contents using the proposed method. By using an acetylation treatment of the fibres it was possible to improve the strength of the composite samples soaked in the water by more than 30%.

Place, publisher, year, edition, pages
Elsevier, 2017
Keywords
CT analysis, Wood fibres, PLA, Adhesion, Acetylation
National Category
Medical Materials Biomaterials Science
Research subject
Engineering Science with specialization in Materials Science
Identifiers
urn:nbn:se:uu:diva-335525 (URN)10.1016/j.indcrop.2016.10.009 (DOI)000390621600006 ()
Funder
Swedish Research Council Formas, 232-2014-202
Available from: 2017-12-06 Created: 2017-12-06 Last updated: 2018-09-14Bibliographically approved
Malmberg, F., Luengo Hendriks, C. L. & Strand, R. (2017). Exact Evaluation of Targeted Stochastic Watershed Cuts. Discrete Applied Mathematics, 216(2), 449-460
Open this publication in new window or tab >>Exact Evaluation of Targeted Stochastic Watershed Cuts
2017 (English)In: Discrete Applied Mathematics, ISSN 0166-218X, E-ISSN 1872-6771, Vol. 216, no 2, p. 449-460Article in journal (Refereed) Published
Abstract [en]

Seeded segmentation with minimum spanning forests, also known as segmentation by watershed cuts, is a powerful method for supervised image segmentation. Given that correct segmentation labels are provided for a small set of image elements, called seeds, the watershed cut method completes the labeling for all image elements so that the boundaries between different labels are optimally aligned with salient edges in the image. Here, a randomized version of watershed segmentation, the targeted stochastic watershed, is proposed for performing multi-label targeted image segmentation with stochastic seed input. The input to the algorithm is a set of probability density functions (PDFs), one for each segmentation label, defined over the pixels of the image. For each pixel, we calculate the probability that the pixel is assigned a given segmentation label in seeded watershed segmentation with seeds drawn from the input PDFs. We propose an efficient algorithm (quasi-linear with respect to the number of image elements) for calculating the desired probabilities exactly.

Place, publisher, year, edition, pages
Elsevier, 2017
Keywords
Image segmentation, Stochastic watershed, Watershed cut, Minimum spanning forest
National Category
Computer Sciences Computer Vision and Robotics (Autonomous Systems)
Research subject
Computerized Image Processing
Identifiers
urn:nbn:se:uu:diva-333808 (URN)10.1016/j.dam.2016.01.006 (DOI)000390504100011 ()
Available from: 2017-11-17 Created: 2017-11-17 Last updated: 2018-09-04Bibliographically approved
Fakhrzadeh, A., Sporndly-Nees, E., Ekstedt, E., Holm, L. & Luengo Hendriks, C. L. (2017). New computerized staging method to analyze mink testicular tissue in environmental research. Environmental Toxicology and Chemistry, 36(1), 156-164
Open this publication in new window or tab >>New computerized staging method to analyze mink testicular tissue in environmental research
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2017 (English)In: Environmental Toxicology and Chemistry, ISSN 0730-7268, E-ISSN 1552-8618, Vol. 36, no 1, p. 156-164Article in journal (Refereed) Published
Abstract [en]

Histopathology of testicular tissue is considered to be the most sensitive tool to detect adverse effects on male reproduction. When assessing tissue damage, seminiferous epithelium needs to be classified into different stages to detect certain cell damages; but stage identification is a demanding task. The authors present a method to identify the 12 stages in mink testicular tissue. The staging system uses Gata-4 immunohistochemistry to visualize acrosome development and proved to be both intraobserver-reproducible and interobserver-reproducible with a substantial agreement of 83.6% (kappa=0.81) and 70.5% (kappa=0.67), respectively. To further advance and objectify this method, they present a computerized staging system that identifies these 12 stages. This program has an agreement of 52.8% (kappa 0.47) with the consensus staging by 2 investigators. The authors propose a pooling of the stages into 5 groups based on morphology, stage transition, and toxicologically important endpoints. The computerized program then reached a substantial agreement of 76.7% (kappa=0.69). The computerized staging tool uses local ternary patterns to describe the texture of the tubules and a support vector machine classifier to learn which textures correspond to which stages. The results have the potential to modernize the tedious staging process required in toxicological evaluation of testicular tissue, especially if combined with whole-slide imaging and automated tubular segmentation. Environ Toxicol Chem 2017;36:156-164.

Keywords
Male reproductive toxicology, Endocrine disruptor, Computational toxicology, Histopathology, Method
National Category
Medical Image Processing
Identifiers
urn:nbn:se:uu:diva-315061 (URN)10.1002/etc.3517 (DOI)000391029800021 ()27271123 (PubMedID)
Available from: 2017-03-03 Created: 2017-03-03 Last updated: 2017-11-29Bibliographically approved
Borodulina, S., Wernersson, E. L. G., Kulachenko, A. & Luengo Hendriks, C. L. (2016). Extracting fiber and network connectivity data using microtomography images of paper. Nordic Pulp & Paper Research Journal, 31(3), 469-478
Open this publication in new window or tab >>Extracting fiber and network connectivity data using microtomography images of paper
2016 (English)In: Nordic Pulp & Paper Research Journal, ISSN 0283-2631, E-ISSN 2000-0669, Vol. 31, no 3, p. 469-478Article in journal (Refereed) Published
National Category
Computer Vision and Robotics (Autonomous Systems) Wood Science
Research subject
Computerized Image Processing
Identifiers
urn:nbn:se:uu:diva-320483 (URN)10.3183/NPPRJ-2016-31-03-p469-478 (DOI)000387976000007 ()
Available from: 2016-09-30 Created: 2017-04-20 Last updated: 2018-01-13Bibliographically approved
Hall, H. C., Fakhrzadeh, A., Luengo Hendriks, C. L. & Fischer, U. (2016). Precision automation of cell type classification and sub-cellular fluorescence quantification from laser scanning confocal images. Frontiers in Plant Science, 7, Article ID 119.
Open this publication in new window or tab >>Precision automation of cell type classification and sub-cellular fluorescence quantification from laser scanning confocal images
2016 (English)In: Frontiers in Plant Science, ISSN 1664-462X, E-ISSN 1664-462X, Vol. 7, article id 119Article in journal (Refereed) Published
Abstract [en]

While novel whole-plant phenotyping technologies have been successfully implemented into functional genomics and breeding programs, the potential of automated phenotyping with cellular resolution is largely unexploited. Laser scanning confocal microscopy has the potential to close this gap by providing spatially highly resolved images containing anatomic as well as chemical information on a subcellular basis. However, in the absence of automated methods, the assessment of the spatial patterns and abundance of fluorescent markers with subcellular resolution is still largely qualitative and time-consuming. Recent advances in image acquisition and analysis, coupled with improvements in microprocessor performance, have brought such automated methods within reach, so that information from thousands of cells per image for hundreds of images may be derived in an experimentally convenient time-frame. Here, we present a MATLAB-based analytical pipeline to (1) segment radial plant organs into individual cells, (2) classify cells into cell type categories based upon Random Forest classification, (3) divide each cell into sub-regions, and (4) quantify fluorescence intensity to a subcellular degree of precision for a separate fluorescence channel. In this research advance, we demonstrate the precision of this analytical process for the relatively complex tissues of Arabidopsis hypocotyls at various stages of development. High speed and robustness make our approach suitable for phenotyping of large collections of stem-like material and other tissue types.

Keywords
automated image analysis; confocal microscopy; Arabidopsis; hypocotyl; automated phenotyping; code:matlab
National Category
Plant Biotechnology
Research subject
Computerized Image Processing
Identifiers
urn:nbn:se:uu:diva-252412 (URN)10.3389/fpls.2016.00119 (DOI)000369802700001 ()
Funder
Bio4EnergyVINNOVA
Available from: 2016-02-09 Created: 2015-05-06 Last updated: 2017-12-04Bibliographically approved
Cadenas, J. O., Megson, G. M. & Luengo Hendriks, C. L. (2016). Preconditioning 2D integer data for fast convex hull computations. PLoS ONE, 11(3), Article ID e0149860.
Open this publication in new window or tab >>Preconditioning 2D integer data for fast convex hull computations
2016 (English)In: PLoS ONE, ISSN 1932-6203, E-ISSN 1932-6203, Vol. 11, no 3, article id e0149860Article in journal (Refereed) Published
National Category
Computer Sciences
Research subject
Computerized Image Processing
Identifiers
urn:nbn:se:uu:diva-282813 (URN)10.1371/journal.pone.0149860 (DOI)000371735200040 ()26938221 (PubMedID)
Available from: 2016-03-03 Created: 2016-04-07 Last updated: 2018-01-10Bibliographically approved
Curic, V., Lefèvre, S. & Luengo Hendriks, C. L. (2015). Adaptive hit or miss transform. In: Mathematical Morphology and Its Applications to Signal and Image Processing: . Paper presented at ISMM 2015, May 27–29, Reykjavik, Iceland (pp. 741-752). Springer
Open this publication in new window or tab >>Adaptive hit or miss transform
2015 (English)In: Mathematical Morphology and Its Applications to Signal and Image Processing, Springer, 2015, p. 741-752Conference paper, Published paper (Refereed)
Abstract [en]

The Hit or Miss Transform is a fundamental morphological operator, and can be used for template matching. In this paper, we present a framework for adaptive Hit or Miss Transform, where structuring elements are adaptive with respect to the input image itself. We illustrate the difference between the new adaptive Hit or Miss Transform and the classical Hit or Miss Transform. As an example of its usefulness, we show how the new adaptive Hit or Miss Transform can detect particles in single molecule imaging.

Place, publisher, year, edition, pages
Springer, 2015
Series
Lecture Notes in Computer Science ; 9082
National Category
Computer Vision and Robotics (Autonomous Systems)
Research subject
Computerized Image Processing
Identifiers
urn:nbn:se:uu:diva-254744 (URN)10.1007/978-3-319-18720-4_62 (DOI)000362366800062 ()978-3-319-18719-8 (ISBN)
Conference
ISMM 2015, May 27–29, Reykjavik, Iceland
Available from: 2015-06-10 Created: 2015-06-10 Last updated: 2018-01-11Bibliographically approved
Spörndly-Nees, E., Ekstedt, E., Magnusson, U., Fakhrzadeh, A., Luengo Hendriks, C. L. & Holm, L. (2015). Effect of pre-fixation delay and freezing on mink testicular endpoints for environmental research. PLoS ONE, 10(5), Article ID e0125139.
Open this publication in new window or tab >>Effect of pre-fixation delay and freezing on mink testicular endpoints for environmental research
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2015 (English)In: PLoS ONE, ISSN 1932-6203, E-ISSN 1932-6203, Vol. 10, no 5, article id e0125139Article in journal (Refereed) Published
National Category
Veterinary Science
Research subject
Computerized Image Processing
Identifiers
urn:nbn:se:uu:diva-252410 (URN)10.1371/journal.pone.0125139 (DOI)000353887100081 ()25933113 (PubMedID)
Available from: 2015-05-01 Created: 2015-05-06 Last updated: 2017-12-04Bibliographically approved
Selig, B., Malmberg, F. & Luengo Hendriks, C. L. (2015). Fast evaluation of the robust stochastic watershed. In: Mathematical Morphology and Its Applications to Signal and Image Processing: . Paper presented at ISMM 2015, May 27–29, Reykjavik, Iceland (pp. 705-716). Springer
Open this publication in new window or tab >>Fast evaluation of the robust stochastic watershed
2015 (English)In: Mathematical Morphology and Its Applications to Signal and Image Processing, Springer, 2015, p. 705-716Conference paper, Published paper (Refereed)
Abstract [en]

The stochastic watershed is a segmentation algorithm that estimates the importance of each boundary by repeatedly segmenting the image using a watershed with randomly placed seeds. Recently, this algorithm was further developed in two directions: (1) The exact evaluation algorithm efficiently produces the result of the stochastic watershed with an infinite number of repetitions. This algorithm computes the probability for each boundary to be found by a watershed with random seeds, making the result deterministic and much faster. (2) The robust stochastic watershed improves the usefulness of the segmentation result by avoiding false edges in large regions of uniform intensity. This algorithm simply adds noise to the input image for each repetition of the watershed with random seeds. In this paper, we combine these two algorithms into a method that produces a segmentation result comparable to the robust stochastic watershed, with a considerably reduced computation time. We propose to run the exact evaluation algorithm three times, with uniform noise added to the input image, to produce three different estimates of probabilities for the edges. We combine these three estimates with the geometric mean. In a relatively simple segmentation problem, F-measures averaged over the results on 46 images were identical to those of the robust stochastic watershed, but the computation times were an order of magnitude shorter.

Place, publisher, year, edition, pages
Springer, 2015
Series
Lecture Notes in Computer Science ; 9082
Keywords
Stochastic watershed, Watershed cuts, Monte Carlo simulations
National Category
Computer Vision and Robotics (Autonomous Systems)
Research subject
Computerized Image Processing
Identifiers
urn:nbn:se:uu:diva-254743 (URN)10.1007/978-3-319-18720-4_59 (DOI)000362366800059 ()978-3-319-18719-8 (ISBN)
Conference
ISMM 2015, May 27–29, Reykjavik, Iceland
Available from: 2015-06-10 Created: 2015-06-10 Last updated: 2018-01-11Bibliographically approved
Selig, B., Vermeer, K. A., Rieger, B., Hillenaar, T. & Luengo Hendriks, C. L. (2015). Fully automatic evaluation of the corneal endothelium from in vivo confocal microscopy. BMC Medical Imaging, 15, Article ID 13.
Open this publication in new window or tab >>Fully automatic evaluation of the corneal endothelium from in vivo confocal microscopy
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2015 (English)In: BMC Medical Imaging, ISSN 1471-2342, E-ISSN 1471-2342, Vol. 15, article id 13Article in journal (Refereed) Published
National Category
Medical Image Processing
Research subject
Computerized Image Processing
Identifiers
urn:nbn:se:uu:diva-254738 (URN)10.1186/s12880-015-0054-3 (DOI)000355507100001 ()
Available from: 2015-04-26 Created: 2015-06-10 Last updated: 2017-12-04Bibliographically approved
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