Graph-cut based interactive segmentation of 3D materials-science images

Waggoner, Jarrell; Zhou, Yunxuan; Simmons, Jeff; Graef, Marc De; Wang, Song

doi:10.1007/s00138-014-0616-3

Cited by 6 publications

(3 citation statements)

References 36 publications

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“…Accurate segmentation of materials images introduces additional challenges to those encountered in other domains (e.g. bio-medical) and has been the topic of a recent special session [2] and recent references [3,4]. Some of these challenges include:…”

Section: Introductionmentioning

confidence: 98%

Segmentation and learning in the quantitative analysis of microscopy images

2015

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In material science and bio-medical domains the quantity and quality of microscopy images is rapidly increasing and there is a great need to automatically detect, delineate and quantify particles, grains, cells, neurons and other functional "objects" within these images. These are challenging problems for image processing because of the variability in object appearance that inevitably arises in real world image acquisition and analysis. One of the most promising (and practical) ways to address these challenges is interactive image segmentation. These algorithms are designed to incorporate input from a human operator to tailor the segmentation method to the image at hand. Interactive image segmentation is now a key tool in a wide range of applications in microscopy and elsewhere. Historically, interactive image segmentation algorithms have tailored segmentation on an image-by-image basis, and information derived from operator input is not transferred between images. But recently there has been increasing interest to use machine learning in segmentation to provide interactive tools that accumulate and learn from the operator input over longer periods of time. These new learning algorithms reduce the need for operator input over time, and can potentially provide a more dynamic balance between customization and automation for different applications. This paper reviews the state of the art in this area, provides a unified view of these algorithms, and compares the segmentation performance of various design choices.

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Section: Introductionmentioning

confidence: 98%

Segmentation and learning in the quantitative analysis of microscopy images

2015

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“…But it will take a long time for manual refinement. (Waggoner et al, 2013(Waggoner et al, , 2014 presented a more elegant architecture, "segmentation propagation", a graph-cut based segmentation method (Boykov & Kolmogorov, 2004;Kolmogorov & Zabin, 2004) which minimizes the energy set based on 3D information between slices, but can not recover the vague and missing boundary. Beside, with the slice's resolution increases, the computation time and memory consumption grow exponentially.…”

Section: Introductionmentioning

confidence: 99%

Fast-FineCut: Grain boundary detection in microscopic images considering 3D information

Ban

et al. 2019

Micron

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The inner structure of a material is called its microstructure. It stores the genesis of a material and determines all the physical and chemical properties. However, the microstructure is highly complex and numerous image defects such as vague or missing boundaries formed during sample preparation, which makes it difficult to extract the grain boundaries precisely. In this work, we address the task of grain boundary detection in microscopic image processing and develop a graph-cut based method called Fast-FineCut to solve the problem. Our algorithm makes two key contributions: (1) An improved approach that incorporates 3D information between slices as domain knowledge, which can detect the boundaries precisely, even for the vague and missing boundaries. (2) A local processing method based on overlap-tile strategy, which can not only solve the "chain scission" problem at the edge of images, but also economize on the consumption of computing resources. We conduct experiments on a stack of 296 slices of microscopic images of polycrystalline iron (1600 × 2800) and compare the performance against several state-of-the-art boundary detection methods. We conclude that Fast-FineCut can detect boundaries effectively and efficiently.

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“…Despite its maturity in computer vision applications, graph cutting has not found wide application within the materials science community. One notable exception is the work of Waggoner et al, where a graph-cut formulation was successfully applied to segment grains from 3D serial sectioned polycrystalline datasets by propagating features from a template or one segmented image to the others in the stack (Waggoner et al, 2013, 2014). In the present work, we present a new framework which is particularly suited to clustering and phase segmentation of microscopy images and multi-modal materials datasets.…”

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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Graph-cut based interactive segmentation of 3D materials-science images

Cited by 6 publications

References 36 publications

Segmentation and learning in the quantitative analysis of microscopy images

Segmentation and learning in the quantitative analysis of microscopy images

Fast-FineCut: Grain boundary detection in microscopic images considering 3D information

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

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