Dyslexia diagnostics by 3D texture analysis of cerebral white matter gyrifications

El-Baz, Ayman; Casanova,; Gimel’farb, Georgy; Mott,; Switala, Andrew E.; Vanbogaert,; McCracken,

doi:10.1109/icpr.2008.4760971

Cited by 12 publications

(8 citation statements)

References 7 publications

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“…The resulting proportions of segmented volumes are used to differentiate between Alzheimer's dementia, epilepsy, or hydrocephalus. 3-D texture analysis is used in El-Baz et al (2008) to segment the white matter and further analyze the shape of its gyrifications as indicators of dyslexia. In Goldbach et al (1991) and Yousefi et al (2011), Markov random fields (MRF) are used to create probability density function maps of gray and white matter as well as cerebrospinal fluid.…”

Section: Brainmentioning

confidence: 99%

“…Depeursinge et al / Medical Image Analysis 18 (2014) 176-196unconstrained and depends on the mapping between the nodes of the graph and the 3-D data. 3-D Markov-Gibbs random field texture models are used by El-Baz et al (2008), where co-occurrence of the voxels' gray-level values are modeled by joint Gibbs probability distributions within spherical neighborhoods.…”

Section: Markov Probabilistic Modelsmentioning

confidence: 99%

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Three-dimensional solid texture analysis in biomedical imaging: Review and opportunities

Depeursinge

Foncubierta–Rodríguez

Ville

et al. 2014

Medical Image Analysis

200

146

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a b s t r a c tThree-dimensional computerized characterization of biomedical solid textures is key to large-scale and high-throughput screening of imaging data. Such data increasingly become available in the clinical and research environments with an ever increasing spatial resolution.In this text we exhaustively analyze the state-of-the-art in 3-D biomedical texture analysis to identify the specific needs of the application domains and extract promising trends in image processing algorithms. The geometrical properties of biomedical textures are studied both in their natural space and on digitized lattices. It is found that most of the tissue types have strong multi-scale directional properties, that are well captured by imaging protocols with high resolutions and spherical spatial transfer functions. The information modeled by the various image processing techniques is analyzed and visualized by displaying their 3-D texture primitives. We demonstrate that non-convolutional approaches are expected to provide best results when the size of structures are inferior to five voxels. For larger structures, it is shown that only multi-scale directional convolutional approaches that are non-separable allow for an unbiased modeling of 3-D biomedical textures. With the increase of high-resolution isotropic imaging protocols in clinical routine and research, these models are expected to best leverage the wealth of 3-D biomedical texture analysis in the future. Future research directions and opportunities are proposed to efficiently model personalized image-based phenotypes of normal biomedical tissue and its alterations. The integration of the clinical and genomic context is expected to better explain the intra class variation of healthy biomedical textures. Using texture synthesis, this provides the exciting opportunity to simulate and visualize texture atlases of normal ageing process and disease progression for enhanced treatment planning and clinical care management.

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Section: Brainmentioning

confidence: 99%

Section: Markov Probabilistic Modelsmentioning

confidence: 99%

Three-dimensional solid texture analysis in biomedical imaging: Review and opportunities

Depeursinge

Foncubierta–Rodríguez

Ville

et al. 2014

Medical Image Analysis

200

146

View full text Add to dashboard Cite

show abstract

“…• Investigating the integration of the proposed work with the BioImaging lab work for the detection of brain disorders such as autism and dyslexia [153][154][155][156][157][158][159][160][161][162][163][164][165].…”

Section: Chapter VI Conclusion and Future Workmentioning

confidence: 99%

A non-invasive image based system for early diagnosis of prostate cancer.

Naef¹,

Firjani²

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“…• Applying these NMF-based frameworks to other medical image analysis applications such as dyslexia [87,109,120,[270][271][272][273][274][275] …”

Section: Chapter IV Conclusion and Future Workmentioning

confidence: 99%

“…In medical image processing, more sophisticated shape models can be integrated to provide more accurate segmentation [109][110][111][112][113][114][115][116][117][118][119][120][121][122][123]. ASMs, developed by Cootes et al [95], are popular DMs that allow for a compact representation of an object's shape that adjusts for shape variance, but still maintain their general shape [124].…”

Section: A In-vitro Prostate Cancer Diagnostic Technologiesmentioning

confidence: 99%

A novel NMF-based DWI CAD framework for prostate cancer.

McClure¹

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Dyslexia diagnostics by 3D texture analysis of cerebral white matter gyrifications

Cited by 12 publications

References 7 publications

Three-dimensional solid texture analysis in biomedical imaging: Review and opportunities

Three-dimensional solid texture analysis in biomedical imaging: Review and opportunities

A non-invasive image based system for early diagnosis of prostate cancer.

A novel NMF-based DWI CAD framework for prostate cancer.

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