Optimizing Texture Primitives Description Based on Variography and Mathematical Morphology

Kourgli, Assia; Belhadj-Aissa, Aichouche; Bouchemakh, Lynda

doi:10.1007/978-3-540-30125-7_107

Cited by 4 publications

(8 citation statements)

References 9 publications

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“…; Christakos ; Kourgli et al. ; Blewett and Kildluff ; Caers ). The variogram, a measure of spatial correlation, is a central tool in geostatistics and can be used for exploratory data analysis (EDA) (Gringarten and Deutsch ).…”

Section: Methodsmentioning

confidence: 99%

“…The variogram measures spatial continuity (or discontinuity), which in the case of 3D structures can be interpreted as surface complexity (Kourgli et al. ; Trevisani et al. ).…”

Section: Methodsmentioning

confidence: 99%

“…Geostatistics provides algorithms for characterizing, modeling, and simulating multidimensional data in a variety of disciplines (Conan et al 1992;Christakos 2000;Kourgli et al 2004;Blewett and Kildluff 2006;Caers 2010). The variogram, a measure of spatial correlation, is a central tool in geostatistics and can be used for exploratory data analysis (EDA) (Gringarten and Deutsch 1999).…”

Section: Empirical Variogramsmentioning

confidence: 99%

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A MS‐lesion pattern discrimination plot based on geostatistics

et al. 2016

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IntroductionA geostatistical approach to characterize MS‐lesion patterns based on their geometrical properties is presented.MethodsA dataset of 259 binary MS‐lesion masks in MNI space was subjected to directional variography. A model function was fit to express the observed spatial variability in x, y, z directions by the geostatistical parameters Range and Sill.ResultsParameters Range and Sill correlate with MS‐lesion pattern surface complexity and total lesion volume. A scatter plot of ln(Range) versus ln(Sill), classified by pattern anisotropy, enables a consistent and clearly arranged presentation of MS‐lesion patterns based on geometry: the so‐called MS‐Lesion Pattern Discrimination Plot.ConclusionsThe geostatistical approach and the graphical representation of results are considered efficient exploratory data analysis tools for cross‐sectional, follow‐up, and medication impact analysis.

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Section: Methodsmentioning

confidence: 99%

“…The variogram measures spatial continuity (or discontinuity), which in the case of 3D structures can be interpreted as surface complexity (Kourgli et al. ; Trevisani et al. ).…”

Section: Methodsmentioning

confidence: 99%

Section: Empirical Variogramsmentioning

confidence: 99%

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A MS‐lesion pattern discrimination plot based on geostatistics

et al. 2016

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“…Because both the spatial dimension h and the variable values Z ( x ), Z ( x + h ) enter equation , the empirical semivariogram value γ ( h ) is a measure of the spatial correlation of the variable gray level . Repeating this calculation for all possible pixel‐pair distances in the image and plotting γ( h ) against the distances h , the empirical semivariogram graph (to avoid excessive jargon, only the term variogram will be used) is generated that describes the spatial structure of the variable gray level in the image . Because in most natural structures closer observations are more similar than distant ones, the associated variograms have low values of γ at small distances, indicating low variability (=strong correlation) of near‐by observations.…”

Section: Methodsmentioning

confidence: 99%

“…Repeating this calculation for all possible pixel‐pair distances in the image and plotting γ( h ) against the distances h , the empirical semivariogram graph (to avoid excessive jargon, only the term variogram will be used) is generated that describes the spatial structure of the variable gray level in the image . Because in most natural structures closer observations are more similar than distant ones, the associated variograms have low values of γ at small distances, indicating low variability (=strong correlation) of near‐by observations. With increasing distance, the value of γ rises, indicating progressively higher variability (=weaker correlation).…”

Section: Methodsmentioning

confidence: 99%

Usability and Potential of Geostatistics for Spatial Discrimination of Multiple Sclerosis Lesion Patterns

Marschallinger

Golaszewski

Kunz

et al. 2013

Journal of Neuroimaging

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Geostatistical Analysis of White Matter Lesions in Multiple Sclerosis Identifies Gender Differences in Lesion Evolution

Marschallinger

Mühlau

Pongratz

et al. 2018

Front. Mol. Neurosci.

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Multiple sclerosis (MS) is a chronic inflammatory demyelinating disease of the central nervous system with presumed autoimmune origin. The development of lesions within the gray matter and white matter, which are highly variable with respect to number, total volume, morphology and spatial evolution and which only show a limited correlation with clinical disability, is a hallmark of the disease. Population-based studies indicate a distinct outcome depending on gender. Here, we studied gender-related differences in the evolution of white matter MS-lesions (MS-WML) in early MS by using geostatistical methods. Within a 3 years observation period, a female and a male MS patient group received disease modifying drugs and underwent standardized annual brain magnetic resonance imaging, accompanied by neurological examination. MS-WML were automatically extracted and the derived binary lesion masks were subject to geostatistical analysis, yielding quantitative spatial-statistics metrics on MS-WML pattern morphology and total lesion volume (TLV). Through the MS-lesion pattern discrimination plot, the following differences were disclosed: corresponding to gender and MS-WML pattern morphology at baseline, two female subgroups (F1, F2) and two male subgroups (M1, M2) are discerned that follow a distinct MS-WML pattern evolution in space and time. F1 and M1 start with medium-level MS-WML pattern smoothness and TLV, both behave longitudinally quasi-static. By contrast, F2 and M2 start with high-level MS-WML pattern smoothness and medium-level TLV. F2 and M2 longitudinal development is characterized by strongly diminishing MS-WML pattern smoothness and TLV, i.e., continued shrinking and break-up of MS-WML. As compared to the male subgroup M2, the female subgroup F2 shows continued, increased MS-WML pattern smoothness and TLV. Data from neurological examination suggest a correlation of MS-WML pattern morphology metrics and EDSS. Our results justify detailed studies on gender-related differences.

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Optimizing Texture Primitives Description Based on Variography and Mathematical Morphology

Cited by 4 publications

References 9 publications

A MS‐lesion pattern discrimination plot based on geostatistics

A MS‐lesion pattern discrimination plot based on geostatistics

Usability and Potential of Geostatistics for Spatial Discrimination of Multiple Sclerosis Lesion Patterns

Geostatistical Analysis of White Matter Lesions in Multiple Sclerosis Identifies Gender Differences in Lesion Evolution

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