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A signature dissimilarity measure for trabecular bone texture in knee radiographs
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10.1118/1.3373522
/content/aapm/journal/medphys/37/5/10.1118/1.3373522
http://aip.metastore.ingenta.com/content/aapm/journal/medphys/37/5/10.1118/1.3373522

Figures

Image of FIG. 1.
FIG. 1.

Texture images and their respective roughness signatures: [(a) and (b)] A reptile skin texture taken from Brodatz album; [(c) and (d)] an isotropic fractal texture image with FD 2.9.

Image of FIG. 2.
FIG. 2.

Steps required for the selection of edge angles in texture image: (a) A trabecular bone texture image; [(b) and (c)] minimum (bright patches represent hills) and maximum (bright patches represent valleys) values of the Laplacian operator calculated for the bone texture image; (d) a binary image of the minimum and maximum values of (white regions represent minima; black regions represent maxima); (e) edges represented as a perimeter of the binary image; and (f) the bone texture image with superimposed edges. Edge angles are selected at the locations of edges.

Image of FIG. 3.
FIG. 3.

Brodatz texture images and their respective orientation signatures: [(a), (b), (d), and (e)] Leather texture images rotated at 70° and 120°; [(c) and (f)] a weave texture image.

Image of FIG. 4.
FIG. 4.

Examples of TB texture regions extracted from the medial and lateral compartments of the tibia.

Image of FIG. 5.
FIG. 5.

Examples of TB texture images taken from [(a) and (b)] healthy and [(c) and (d)] OA classes.

Image of FIG. 6.
FIG. 6.

Classification accuracies (%) calculated for different parts of tibia head. Two areas corresponding to the highest classification accuracies are highlighted. Square regions with white boundary were selected by the automated method.

Image of FIG. 7.
FIG. 7.

Examples of misclassified images of [(a) and (b)] healthy and [(c) and (d)] OA TB textures.

Tables

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TABLE I.

Classification accuracies of Brodatz textures calculated using four training angles.

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TABLE II.

Classification accuracies of Brodatz textures calculated using single training angle.

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TABLE III.

Mean (99% CI) values of the SDM calculated for different image sizes; 20 images were used for each image size.

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TABLE IV.

Mean (99% CI) values of the SDM calculated for different anisotropy angles; ten images were used for each anisotropy direction.

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TABLE V.

Mean (99% CI) values of the SDM calculated for different noise contribution, no statistically significant differences were found; 20 images were used for each noise type.

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TABLE VI.

Mean (99% CI) values of the SDM calculated for different film screens; 20 images were used for each film screen.

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TABLE VII.

Mean (99% CI) values of the SDM calculated for different exposures, no statistical differences were found; 25 image regions were used for each exposure.

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TABLE VIII.

Mean (99% CI) values of the SDM calculated for different magnifications, no statistical differences were found; 25 image regions were used for each magnification.

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TABLE IX.

Mean (99% CI) values of the SDM calculated for different projection angles; 25 image regions were used for each angle.

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TABLE X.

Details of healthy and OA classes.

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TABLE XI.

Confusion matrices of SDM, WND-CHARM, and TAMURA systems for knee OA detection.

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/content/aapm/journal/medphys/37/5/10.1118/1.3373522
2010-04-14
2014-04-23
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752b84549af89a08dbdd7fdb8b9568b5 journal.articlezxybnytfddd
Scitation: A signature dissimilarity measure for trabecular bone texture in knee radiographs
http://aip.metastore.ingenta.com/content/aapm/journal/medphys/37/5/10.1118/1.3373522
10.1118/1.3373522
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