Ultrasound Image Texture Analysis for Characterizing Intramuscular Fat Content of Live Beef Cattle

The primary factors in determining beef quality grades are the amount and distribution of intramuscular fat percentage (IMFAT). Texture analysis was applied to ultrasound B-mode images from ribeye muscle of live beef cattle to predict its IMFAT. We used wavelet transform (WT) for multiresolutional texture analysis and second-order statistics using a gray-level co-occurrence matrix (GLCM) technique. Sets of WT-and GLCM-based texture features were calculated from ultrasonic images from 207 animals and linear regression methods were used for IMFAT prediction. WT-based features included energy ratios, central moments of wavelet-decomposed subimages and wavelet edge density. The regression model using WT features provided a root mean square error (RMSE) of 1.44 for prediction of IMFAT using validation images, while that of GLCM features provided an RMSE of 1.90. The prediction models using the WT features showed potential for objective quality evaluation in the live animals.

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Ultrasound image texture analysis of the intima and media layers of the common carotid artery and its correlation with age and gender

Computerized Medical Imaging and Graphics ◽

10.1016/j.compmedimag.2009.02.005 ◽

2009 ◽

Vol 33 (4) ◽

pp. 317-324 ◽

Cited By ~ 37

Author(s):

C.P. Loizou ◽

M. Pantziaris ◽

M.S. Pattichis ◽

E. Kyriacou ◽

C.S. Pattichis

Keyword(s):

Carotid Artery ◽

Texture Analysis ◽

Common Carotid Artery ◽

Ultrasound Image ◽

Image Texture ◽

Age And Gender ◽

Image Texture Analysis ◽

The Common ◽

And Gender

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Ultrasound image texture analysis for liver fibrosis stage diagnostics

Programming and Computer Software ◽

10.1134/s0361768815050059 ◽

2015 ◽

Vol 41 (5) ◽

pp. 273-278 ◽

Cited By ~ 5

Author(s):

A. V. Kvostikov ◽

A. S. Krylov ◽

U. R. Kamalov

Keyword(s):

Liver Fibrosis ◽

Texture Analysis ◽

Ultrasound Image ◽

Fibrosis Stage ◽

Image Texture ◽

Image Texture Analysis

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Improving the Distinction between Benign and Malignant Breast Lesions: The Value of Sonographic Texture Analysis

Ultrasonic Imaging ◽

10.1177/016173469301500401 ◽

1993 ◽

Vol 15 (4) ◽

pp. 267-285 ◽

Cited By ~ 72

Author(s):

Brian S. Garra ◽

Brian H. Krasner ◽

Steven C. Horii ◽

Susan Ascher ◽

Seong K. Mun ◽

...

Keyword(s):

Texture Analysis ◽

Benign Lesion ◽

Ultrasound Image ◽

Image Texture ◽

Breast Lesions ◽

Image Texture Analysis ◽

True Nature ◽

Malignant Breast ◽

Occurrence Matrix ◽

Ultrasound Scanner

To improve the ability of ultrasound to distinguish benign from malignant breast lesions, we used quantitative analysis of ultrasound image texture. Eight cancers, 22 cysts, 28 fibroadenomata, and 22 fibrocystic nodules were studied. The true nature of each lesion was determined by aspiration (for some cysts) or by open biopsy. Analysis of image texture was performed on digitized video output from the ultrasound scanner using fractal analysis and statistical texture analysis methods. The most useful features were those derived from co-occurrence matrices of the images. Using two features together (contrast of a co-occurrence matrix taken in an oblique direction, and correlation of a co-occurrence matrix taken in the horizontal direction), it was possible to exclude 78% of fibroadenomata, 73% of cysts, and 91% of fibrocystic nodules while maintaining 100% sensitivity for cancer. These findings suggest that ultrasonic image texture analysis is a simple way to markedly reduce the number of benign lesion biopsies without missing additional cancers.

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The Effect of Rider:Horse Bodyweight Ratio on the Superficial Body Temperature of Horse’s Thoracolumbar Region Evaluated by Advanced Thermal Image Processing

Animals ◽

10.3390/ani12020195 ◽

2022 ◽

Vol 12 (2) ◽

pp. 195

Author(s):

Małgorzata Domino ◽

Marta Borowska ◽

Anna Trojakowska ◽

Natalia Kozłowska ◽

Łukasz Zdrojkowski ◽

...

Keyword(s):

Texture Analysis ◽

Texture Features ◽

Image Texture ◽

Gray Level ◽

Image Texture Analysis ◽

Thermal Images ◽

Thoracolumbar Region ◽

Thermal Features ◽

Occurrence Matrix ◽

The Impact

Appropriate matching of rider–horse sizes is becoming an increasingly important issue of riding horses’ care, as the human population becomes heavier. Recently, infrared thermography (IRT) was considered to be effective in differing the effect of 10.6% and 21.3% of the rider:horse bodyweight ratio, but not 10.1% and 15.3%. As IRT images contain many pixels reflecting the complexity of the body’s surface, the pixel relations were assessed by image texture analysis using histogram statistics (HS), gray-level run-length matrix (GLRLM), and gray level co-occurrence matrix (GLCM) approaches. The study aimed to determine differences in texture features of thermal images under the impact of 10–12%, >12 ≤15%, >15 <18% rider:horse bodyweight ratios, respectively. Twelve horses were ridden by each of six riders assigned to light (L), moderate (M), and heavy (H) groups. Thermal images were taken pre- and post-standard exercise and underwent conventional and texture analysis. Texture analysis required image decomposition into red, green, and blue components. Among 372 returned features, 95 HS features, 48 GLRLM features, and 96 GLCH features differed dependent on exercise; whereas 29 HS features, 16 GLRLM features, and 30 GLCH features differed dependent on bodyweight ratio. Contrary to conventional thermal features, the texture heterogeneity measures, InvDefMom, SumEntrp, Entropy, DifVarnc, and DifEntrp, expressed consistent measurable differences when the red component was considered.

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