Spatially and Intensity Adaptive Morphology

Pinoli, Jean‐Charles; Debayle, Johan

doi:10.1109/jstsp.2012.2214762

Cited by 8 publications

(6 citation statements)

References 43 publications

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“…The challenge is to improve image quality while retaining essential information about the true structure. In the present study, we apply two main strategies: mathematical morphology (MM) (Pinoli & Debayle, 2012) and nonlinear edge-preserving filtering (Tomasi & Manduchi, 1998). Previously we developed 'F3D', a graphics-card aware image processing plug-in for Fiji (Schindelin et al, 2012) that employs MM and non-linear filters and can handle data sets whose size exceeds the amount of RAM available in the computer system (Ushizima et al, 2014).…”

Section: Image Enhancementmentioning

confidence: 99%

Insight into 3D micro-CT data: exploring segmentation algorithms through performance metrics

Perciano

Ushizima

Krishnan

et al. 2017

J Synchrotron Radiat

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Three-dimensional (3D) micro-tomography (µ-CT) has proven to be an important imaging modality in industry and scientific domains. Understanding the properties of material structure and behavior has produced many scientific advances. An important component of the 3D µ-CT pipeline is image partitioning (or image segmentation), a step that is used to separate various phases or components in an image. Image partitioning schemes require specific rules for different scientific fields, but a common strategy consists of devising metrics to quantify performance and accuracy. The present article proposes a set of protocols to systematically analyze and compare the results of unsupervised classification methods used for segmentation of synchrotron-based data. The proposed dataflow for Materials Segmentation and Metrics (MSM) provides 3D micro-tomography image segmentation algorithms, such as statistical region merging (SRM), k-means algorithm and parallel Markov random field (PMRF), while offering different metrics to evaluate segmentation quality, confidence and conformity with standards. Both experimental and synthetic data are assessed, illustrating quantitative results through the MSM dashboard, which can return sample information such as media porosity and permeability. The main contributions of this work are: (i) to deliver tools to improve material design and quality control; (ii) to provide datasets for benchmarking and reproducibility; (iii) to yield good practices in the absence of standards or ground-truth for ceramic composite analysis.

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Section: Image Enhancementmentioning

confidence: 99%

Insight into 3D micro-CT data: exploring segmentation algorithms through performance metrics

Perciano

Ushizima

Krishnan

et al. 2017

J Synchrotron Radiat

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“…The GANMM has been introduced by Debayle and Pinoli [34]- [36]. The central idea of GANMM is to substitute traditional fixed-shape structural elements (SEs) by adaptive SEs.…”

Section: A Spectral-spatial Classificationmentioning

confidence: 99%

An Integrated Method for Urban Main-Road Centerline Extraction From Optical Remotely Sensed Imagery

Shi

Zhang

Debayle

2014

IEEE Trans. Geosci. Remote Sensing

180

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Road information has a fundamental role in modern society. Road extraction from optical satellite images is an economic and efficient way to obtain and update a transportation database. This paper presents an integrated method to extract urban main-road centerlines from satellite optical images. The proposed method has four main steps. First, general adaptive neighborhood is introduced to implement spectral-spatial classification to segment the images into two categories: road and nonroad groups. Second, road groups and homogeneous property, measured by local Geary's C, are fused to improve road-group accuracy. Third, road shape features are used to extract reliable road segments. Finally, local linear kernel smoothing regression is performed to extract smooth road centerlines. Road networks are then generated using tensor voting. The proposed method is tested and subsequently validated using a large set of multispectral high-resolution images. A comparison with several existing methods shows that the proposed method is more suitable for urban main-road centerline extraction. Index Terms-General adaptive neighborhood (GAN), localGeary's C, local linear kernel smoothing regression, optical remotely sensed images, shape feature, spectral-spatial classification, tensor voting, urban main-road centerline extraction.

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“…In contrast, F3D plug-in enables the user to change these common assumptions, particularly allowing the construction of strel that presents asymmetry and tridimensionality. The geometric definition of the strel is translation invariant in F3D, and we refer to the algorithms in Hedberg et al [14] and Pinoli et al [15] for methods that support spatially-variant structuring elements for binary and intensity-based approaches, respectively. …”

Section: Content-based Structuring Elementsmentioning

confidence: 99%

“…New extensions to those algorithms applied to filamentous structures consider the variation of the structuring elements [14,15], the combination of MM with Hessian matrix analysis [18], and optimally designed algorithms to applications in biology as in [19,20]. Similarly, our paper focuses on the optimization of MM operators for fibrillar structures embedded in large image stacks (3D), considering hybrid architectures for fast processing.…”

Section: Detecting Local-linear Structuresmentioning

confidence: 99%

Structure recognition from high resolution images of ceramic composites

Ushizima

Perciano

Krishnan

et al. 2014

2014 IEEE International Conference on Big Data (Big Data)

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Fibers provide exceptional strength-to-weight ratio capabilities when woven into ceramic composites, transforming them into materials with exceptional resistance to high temperature, and high strength combined with improved fracture toughness. Microcracks are inevitable when the material is under strain, which can be imaged using synchrotron X-ray computed micro-tomography (µ-CT) for assessment of material mechanical toughness variation. An important part of this analysis is to recognize fibrillar features. This paper presents algorithms for detecting and quantifying composite cracks and fiber breaks from high-resolution image stacks. First, we propose recognition algorithms to identify the different structures of the composite, including matrix cracks and fibers breaks. Second, we introduce our package F3D for fast filtering of large 3D imagery, implemented in OpenCL to take advantage of graphic cards. Results show that our algorithms automatically identify micro-damage and that the GPU-based implementation introduced here takes minutes, being 17x faster than similar tools on a typical image file.

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Spatially and Intensity Adaptive Morphology

Cited by 8 publications

References 43 publications

Insight into 3D micro-CT data: exploring segmentation algorithms through performance metrics

Insight into 3D micro-CT data: exploring segmentation algorithms through performance metrics

An Integrated Method for Urban Main-Road Centerline Extraction From Optical Remotely Sensed Imagery

Structure recognition from high resolution images of ceramic composites

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