Contrast-independent curvilinear structure enhancement in 3D biomedical images

Sazak, Çiğdem; Obara, Bogusław

doi:10.1109/isbi.2017.7950723

Cited by 5 publications

(14 citation statements)

References 14 publications

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“…c. Figure 4: Mean ROC curve for all the 9 vascular networks 3D images enhanced using the state-of-the-art approaches alongside the proposed MTHT Vesselness and MTHT Neuiteness (see legend for colours). Correspondingly, the mean AUC values can be found in Table II. PCT (vesselness and neuriteness) [18] and with the latest 3D enhancement approach [15]. Our proposed approach clearly has the highest mean AUC value (0.995) with a standard deviation equal to (0.006) for the proposed MTHT-vesselness.…”

Section: B 3d Vascular Network Complexitymentioning

confidence: 85%

“…where θ j ∈ [0; 180). In 3D, as proposed in [18], [29], a point distribution on the sphere of unit radius is used to define the orientation u j of the 3D line structuring element as follows:…”

Section: B Proposed Methods Frameworkmentioning

confidence: 99%

“…The red box on the original image shows the region of interest. (b) Vesselness[6] , (c) Neuriteness[7], (d) PCT vesselness[18], (e) PCT neuriteness[18], (f) RORPO[15], (g) MTHT vesselness, and (h) MTHT neuriteness.…”

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confidence: 99%

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Curvilinear Structure Enhancement by Multiscale Top-Hat Tensor in 2D/3D Images

Alharbi

Sazak

Nelson

et al. 2018

2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

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A wide range of biomedical applications require enhancement, detection, quantification and modelling of curvilinear structures in 2D and 3D images. Curvilinear structure enhancement is a crucial step for further analysis, but many of the enhancement approaches still suffer from contrast variations and noise. This can be addressed using a multiscale approach that produces a better quality enhancement for low contrast and noisy images compared with a single-scale approach in a wide range of biomedical images. Here, we propose the Multiscale Top-Hat Tensor (MTHT) approach, which combines multiscale morphological filtering with a local tensor representation of curvilinear structures in 2D and 3D images. The proposed approach is validated on synthetic and real data, and is also compared to the state-of-the-art approaches. Our results show that the proposed approach achieves highquality curvilinear structure enhancement in synthetic examples and in a wide range of 2D and 3D images.

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Section: B 3d Vascular Network Complexitymentioning

confidence: 85%

Section: B Proposed Methods Frameworkmentioning

confidence: 99%

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Curvilinear Structure Enhancement by Multiscale Top-Hat Tensor in 2D/3D Images

Alharbi

Sazak

Nelson

et al. 2018

2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

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“…Phase congruency (PC) was first introduced in [16] and later combined with a local tensor to enhance curvilinear structures in 2D [17] and 3D [18] images. The majority of Hessian-based approaches rely on image intensity, which leads to poor enhancement or suppression of finer and lower intensity vessels, where Phase Congruency Tensor-based approaches are image contrast-independent.…”

Section: Phase Congruency Tensor-based Approachesmentioning

confidence: 99%

“…where θ j ∈ [0; 180). In 3D, as proposed in [42,18], a point distribution on the sphere of unit radius is used to define the orientation u j of the 3D line structuring element as follows:…”

Section: Multiscale Top-hat Transformmentioning

confidence: 99%

The multiscale top-hat tensor enables specific enhancement of curvilinear structures in 2D and 3D images

Alharbi

Sazak

Nelson

et al. 2020

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A toolbox to analyze collective cell migration, proliferation and cellular organization simultaneously

Hohmann,

Ghadban,

Prell

et al. 2023

Cell Adhesion & Migration

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Background Analyses of collective cell migration and orientation phenomena are needed to assess the behavior of multicellular clusters. While some tools to the authors’ knowledge none is capable to analyze collective migration, cellular orientation and proliferation in phase contrast images simultaneously. Methods We provide a tool based to analyze phase contrast images of dense cell layers. PIV is used to calculatevelocity fields, while the structure tensor provides cellular orientation. An artificial neural network is used to identify cell division events, allowing to correlate migratory and organizational phenomena with cell density. Conclusion The presented tool allows the simultaneous analysis of collective cell behavior from phase contrast images in terms of migration, (self-)organization and proliferation.

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Contrast-independent curvilinear structure enhancement in 3D biomedical images

Cited by 5 publications

References 14 publications

Curvilinear Structure Enhancement by Multiscale Top-Hat Tensor in 2D/3D Images

Curvilinear Structure Enhancement by Multiscale Top-Hat Tensor in 2D/3D Images

The multiscale top-hat tensor enables specific enhancement of curvilinear structures in 2D and 3D images

A toolbox to analyze collective cell migration, proliferation and cellular organization simultaneously

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