3D reconstruction of medical images from slices automatically landmarked with growing neural models
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Feature-based registration appears more suitable in the case of vessels' contours and centerline, while intensity-based registration can be effectively used for image slices depicting abnormal morphologies such as tumors.
“To that respect, feature-based registration appears more suitable in the case of vessels’ contours and centerline [196], while the intensity-based registration can be effectively used for image slices depicting abnormal morphologies such as tumors [197].”
From AI in Medical Imaging Informatics: Current Challenges and Future Directions (2020) · cites this paper as [197]
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Cites
Reconstruction and representation of 3D objects with radial basis functions
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AI in Medical Imaging Informatics: Current Challenges and Future Directions
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“To that respect, feature-based registration appears more suitable in the case of vessels’ contours and centerline [196], while the intensity-based registration can be effectively used for image slices depicting abnormal morphologies such as tumors [197].”
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“…range of real-world experiments including 3D scene reconstruction, 3D printing, movie making, virtual city, and medical visualization (Agarwal et al., 2011; Li et al., 2012; Snavely et al., 2008; Wojciechowski et al., 2004; Rengier et al., 2010; Angelopoulou et al., 2015; Stahlberg et al., 2015).”
BackgroundA novel feature representation for automatic 3D object recognition in cluttered scenes
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“The evolution of 3D reconstruction techniques grounded in medical imaging harks back to the 1970s, with early scholars pioneering the reconstruction of 3D models from sequential two-dimensional images through ray tracing technology [33].”
Two-and-a-half Order Score-based Model for Solving 3D Ill-posed Inverse Problems
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“Consequently, the inter-slice relationships crucial for achieving coherent and precise 3D reconstructions remain underutilized [34].”
BackgroundRegional frequency analysis using Growing Neural Gas network
Abdi, A., Hassanzadeh, Y., & Ouarda, T. (2017) · doi:10.1016/j.jhydrol.2017.04.047 · Cited by 20
“The GNG algorithm is also gaining increasing interest in a number of other fields such as medicine (Cselényi, 2005; Oliveira Martins et al., 2009; Angelopoulou et al., 2015); robotics (Carlevarino et al., 2000; Ferrer, 2014); economics (Lisboa et al., 2000; Decker, 2005); industrial applications…”
BackgroundMethodInfluential3D character recognition using binocular camera for medical assist
Yang, J.-C., Xu, R., Ding, Z., & Lv, H. (2017) · Neurocomputing · doi:10.1016/j.neucom.2016.01.122 · Cited by 14
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3D Visualization of Brain Tumors Using MR Images: A Survey.
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“2000; Ferrer 2014), medicine (Cselényi 2005; Oliveira Martins et al. 2009; Angelopoulou et al. 2015), and economics (Decker 2005; Lisboa et al.”
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“A. Angelopoulou et al. [10] analyzed the brain portions and established predetermined landmarks on the sections in order to rebuild the 3D surface of the ventricles using the GNG (Growing Neural Gas) method, which also eliminates noise and outliers.”
MethodReconstructing Part-Level 3D Models From a Single Image
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“Recent 3D reconstruction approaches [1, 2] have shown promising results in modeling single image into 3D object, which can serve as prerequisites for many computer vision tasks, e.g. , scene reconstruction[3, 4, 5, 6], medical identification [7, 8] and motion capture [9, 10].”
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Bioinspired and knowledge based techniques and applications
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Multiple View Performers for Shape Completion
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“Shape completion from a single image or two images is a difficult and important problem (see: [2, 3, 4, 5, 6, 7, 8, 9]).”
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Automatic ventricle segmentation in brain MRI of young children
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“[14] utilized self-organizing net (namely, growing neural gas) for selecting landmarks used in ventricle reconstruction.”
MethodA New Set of 3D Shifted Fractional-Order Gegenbauer Descriptors for Volumetric Image Representation
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An Optimal Cleaning Robot for all Kinds of Reflection Pools
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“For instance, in medicine we cite techniques described in [12] and [13], and specifically in Computer Tomography (CT) we have [14] and [15].”
Method
- Authors
- Angelopoulou, A., Psarrou, A., Garcia-Rodriguez, J., Orts-Escolano, S., Azorin-Lopez, J., & Revett, K.
- Affiliations
- University of Westminster (GB)
- British University in Egypt (EG)
- Venue
- Neurocomputing
- Publisher
- Elsevier BV
- Published
- 2015-02
- Volume
- 150
- Pages
- 16-25
- ISSN
- 0925-2312 (ISSN-L)
- OpenAlex
- W2103331978
- Cited by
- 31
- References
- 0
- Citations by year
- 2026: 1 · 2025: 2 · 2024: 1 · 2023: 2 · 2022: 5 · 2021: 3 · 2020: 3 · 2019: 4 · 2018: 3 · 2017: 1 · 2016: 4 · 2015: 1 · 2014: 1
- FWCI
- 2.09
- Type
- journal article
- Language
- en
- Open access
- green
- hdl.handle.net
- Licence
- Locations
- Neurocomputing (publishedVersion)
- WestminsterResearch (University of Westminster) (acceptedVersion)
- LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) (submittedVersion, other-oa)
- Repositorio Institucional de la Universidad de Alicante (Universidad de Alicante) (submittedVersion, other-oa)
- Funders
- Spanish Government (DPI2013-40534-R)
- Valencian Government (GV/2013/005)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Physical Sciences › Engineering › Computational Mechanics › 3D Shape Modeling and Analysis
- Life Sciences › Neuroscience › Neurology › Brain Tumor Detection and Classification
- Keywords
- 3D reconstruction, brain ventricles, growing neural gas, self-organizing maps, T1-weighted imaging, surface reconstruction, ventricle segmentation, outlier tolerance, noise immunity
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