Case study: an evaluation of user-assisted hierarchical watershed segmentation

Cates, Joshua; Whitaker, Ross T.; Jones, Greg

doi:10.1016/j.media.2005.04.007

Cited by 66 publications

(27 citation statements)

References 29 publications

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“…33,34 The 3D structure was additionally separated into particles based upon a three-dimensional watershed algorithm applied to a distance measurement. 35,36 Volume fractions, feature size and size distributions for each individual phase were calculated directly from the 3D reconstruction. The specific surface area was calculated inside the Amira 5.4.5. software using a triangular approximation based on the marching cubes algorithm.…”

Section: D Microstructure Analysismentioning

confidence: 99%

Microstructural evaluation of Ni-SDC cermet from a representative 2D image and/or a 3D reconstruction based on a stack of images

Kapun¹,

Marinšek²,

Merzel³

et al. 2017

Mater. Tehnol.

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This work provides an in-depth discussion on the subject of deriving microstructural parameters from a realistic 2D image or a 3D volume reconstruction based on a stack of images. As a convenient model material, a nickel/samaria-doped-ceria (Ni-SDC) cermet synthetized by the citrate-nitrate combustion reaction is tested. Field-emission scanning electron microscopy (FE-SEM) for 2D microstructure evaluation or focused-ion-beam/scanning-electron-microscopy (FIB-SEM) for 3D imaging is used for the cermet characterization. Microstructural parameters, such as the volume ratio of phases, grain morphology, contiguity of phases and triple-point boundary density, are quantitatively evaluated. These microstructural parameters reveal that a 2D microstructural evaluation provides a relatively accurate and quick analytical approach. However, it is shown that a detailed microstructural quantification of the parameters that are closely related to transport phenomena and electrochemical reactions in a porous cermet is only possible through the three-dimensional (3D) quantitative material characterization. Based on the microstructural evaluation, optimization of the Ni-SDC material used is briefly discussed. Keywords: solid-oxide fuel cell, Ni-SDC anode, microstructure, FIB tomography Ni-SDC kermet (nikelj/cerijev oksid dopiran s samarijevim oksidom) je eden izmed najpogosteje uporabljenih anodnih materialov v gorivnih celicah s trdnim elektrolitom (angl. SOFC). Med pripravo materiala sodi tudi kvantitativno vrednotenje mikrostrukturnih parametrov kot so velikost in porazdelitev velikosti zrn, dele`i faz, kontinuitete, gostota trojnih to~k (angl. TPB), gostota materiala, itd. V tem prispevku`elimo primerjati dva razli~na na~ina analiziranja teh parametrov. Prva t.i. 2D metoda temelji na obdelavi slik, posnetih z vrsti~nim elektronskim mikroskopom s poljsko emisijo (SEM). Medtem, ko 3D metoda upo{teva 3D rekonstrukcijo vzorca iz slik s pomo~jo sistema s fokusiranim ionskim sklopom (FIB-SEM). Omenjeni mikrostrukturni parametri razkrivajo, da je 2D pristop razmeroma hiter in natan~en. Vendar se je izkazalo, da je za natan~nej{o kvantitativno analizo mikrostrukture, ki je tesno povezana s prenosom snovi in elektrokemijskimi reakcijami v anodnem materialu, potreben 3D pristop. Metodi sta bili ocenjeni glede na njuno te`avnost izvedbe in~asovno zahtevnost.

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Section: D Microstructure Analysismentioning

confidence: 99%

Microstructural evaluation of Ni-SDC cermet from a representative 2D image and/or a 3D reconstruction based on a stack of images

Kapun¹,

Marinšek²,

Merzel³

et al. 2017

Mater. Tehnol.

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“…The merge tree between those basins that occur at different flood level thresholds is created by evaluating the height of the common boundary points. The flood level is a value that reflects the amount of metaphorical precipitation that is rained into the catchment basins; its minimum value is zero and its maximum value is the difference between the highest and lowest values in the input image (EDM) [12]. The process is controlled by two parameters: the threshold and the level, both set as a fraction (0.0 -1.0) of the maximum flood level.…”

Section: Top-down Approachmentioning

confidence: 99%

Image Segmentation Techniques for Granular Materials

Fonseca¹,

O’Sullivan

Coop³

2009

AIP Conference Proceedings

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This is the published version of the paper.This version of the publication may differ from the final published version. Abstract. To improve understanding of the mechanical behavior of granular materials it is important to be able to quantify the relative arrangement of the grains, i.e. the fabric. This can be done, for example, by measuring the orientations of the particles (e.g. the long axis orientation) or by considering the orientations of the vectors normal to each grain-grain contact. In two dimensional (2D) analyses this information can be obtained by digital image analysis of images of thin sections obtained from an optical microscope. While such data is useful, granular materials of engineering interest are three dimensional (3D) materials and quantification of the 3D fabric is necessary. Micro ComputedTomography (µCT) together with 3D image analysis has emerged as a promising technique for obtaining the 3D data required. This paper aims to highlight the challenges associated with using image analysis to provide quantitative information on fabric. While automated image segmentation has proved to produce reasonable results in some cases, it is sometimes less successful when dealing with highly irregular and angular soil grains. This paper evaluates the effectiveness of 2D and 3D segmentation techniques that rely on the watershed segmentation algorithm. The primary material considered is Reigate Silver Sand, a natural quartzitic sand with grain diameters in the range of 150-300µm. While the sand considered is primarily of interest to geotechnical engineers, the results of this study will be of interest to anyone seeking to quantify granular material fabric using either 2D microscopy data or µCT 3D data sets. Permanent repository link

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“…This results in a binary image of the neuron membranes, which we then blur with a Gaussian filter to obtain a fuzzy edge map. The final step applies a watershed segmentation using the implementation from ITK [9]. A perfect 2D segmentation is not possible because of the problems with grazed membranes, as described in Section 1, and the presence of intracellular structures.…”

Section: D Segmentationmentioning

confidence: 99%

An optimal-path approach for neural circuit reconstruction

Jurrus

Whitaker

Jones

et al. 2008

2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro

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Neurobiologists are collecting large amounts of electron microscopy image data to gain a better understanding of neuron organization in the central nervous system. Image analysis plays an important role in extracting the connectivity present in these images; however, due to the large size of these datasets, manual analysis is essentially impractical. Automated analysis, however, is challenging because of the difficulty in reliably segmenting individual neurons in 3D. In this paper, we describe an automatic method for finding neurons in sequences of 2D sections. The proposed method formulates the problem of finding paths through sets of sections as an optimal path computation, which applies a cost function to the identification of a cell from one section to the next and solves this optimization problem using Dijkstra's algorithm. This basic formulation allows us to account for variability or inconsistencies between sections and to prioritize cells based on the evidence of their connectivity.

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Case study: an evaluation of user-assisted hierarchical watershed segmentation

Cited by 66 publications

References 29 publications

Microstructural evaluation of Ni-SDC cermet from a representative 2D image and/or a 3D reconstruction based on a stack of images

Microstructural evaluation of Ni-SDC cermet from a representative 2D image and/or a 3D reconstruction based on a stack of images

Image Segmentation Techniques for Granular Materials

An optimal-path approach for neural circuit reconstruction

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