Estimators of Tissue Proportions from X-Ray CT Images

Glasbey, C. A.; Robinson, C. D.

doi:10.1111/j.0006-341x.2002.00928.x

Cited by 24 publications

(11 citation statements)

References 14 publications

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“…Focusing on the muscle -fat density range, pure tissues of either type have only a narrow range of pixel values and many of the pixels can be characterised as mixed pixels (Choi et al, 1991). The possible classification methods were evaluated by Glasbey and Robinson (2002).…”

mentioning

confidence: 99%

Development of a computed tomographic calibration method for the determination of lean meat content in pig carcasses

Romvári

Dobrowolski²,

Romics

et al. 2006

Acta Veterinaria Hungarica

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Sixty left sides of pig carcasses were scanned by spiral computed tomography (CT) to measure lean meat weight and percentage. The carcasses were fully dissected and scanned to develop a calibration protocol. Different image analyses were performed on the basis of anatomically defined scans, direct volumetric estimation, body-and grey-scale ranges and using Partial Least Squares (PLS) regression of data provided by CT. The R 2 values of the calibrations for lean meat weight were 0.874, 0.976, 0.983 and 0.992, respectively, depending on the method applied. The PLS proved to be the best approach with a calibration RSD of 232 g. When changing from lean meat weight to percentage, the statistical goodness drops to a very small extent (R 2 = 0.988, RSD = 0.56). According to the results, the CT method can be recommended as a reference for determining the lean meat content of pig carcasses.

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confidence: 99%

Development of a computed tomographic calibration method for the determination of lean meat content in pig carcasses

Romvári

Dobrowolski²,

Romics

et al. 2006

Acta Veterinaria Hungarica

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“…A highly sophisticated approach to pixel classification has been described 6 . This recognizes “mixed pixels,” i.e.…”

Section: Discussionmentioning

confidence: 99%

“…Extensive work has been carried out in dogs, 3 poultry, 4 pigs, 5 and sheep 6 using CT in body composition studies. This aspect of imaging and the wide range of approaches to the topic in humans have been reviewed, 7 and automated computer-aided approaches, especially for the determination of adipose volumes, have been described for MR images [8][9][10] and CT 11,6 in various species. This paper describes the details of a program written in the ImageJ macro language 12 for the automated measurement of subcutaneous adipose tissue thickness.…”

Section: Introductionmentioning

confidence: 99%

An Application of Image Processing Techniques in Computed Tomography Image Analysis

McEvoy

2007

Vet Radiology Ultrasound

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An estimate of the thickness of subcutaneous adipose tissue at differing positions around the body was required in a study examining body composition. To eliminate human error associated with the manual placement of markers for measurements and to facilitate the collection of data from a large number of animals and image slices, automation of the process was desirable. The open-source and free image analysis program ImageJ was used. A macro procedure was created that provided the required functionality. The macro performs a number of basic image processing procedures. These include an initial process designed to remove the scanning table from the image and to center the animal in the image. This is followed by placement of a vertical line segment from the mid point of the upper border of the image to the image center. Measurements are made between automatically detected outer and inner boundaries of subcutaneous adipose tissue along this line segment. This process was repeated as the image was rotated (with the line position remaining unchanged) so that measurements around the complete circumference were obtained. Additionally, an image was created showing all detected boundary points so that sites of measurement could be checked by visual inspection. This paper provides a detailed account of this macro, illustrates the effect of its components on the image, and the applicability of standard image processing techniques to veterinary imaging.

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“…The range of 210 to þ93 Hu was used in this study. A more sophisticated approach is to take account of mixed pixels, as in Glasbey and Robinson (2002), though in practice the authors found that the differences in estimated areas were very small.…”

Section: Figurementioning

confidence: 99%

In vivo measurements of muscle volume by automatic image analysis of spiral computed tomography scans

Navajas¹,

Glasbey

McLean³

et al. 2006

Anim. Sci.

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This study investigates the accuracy of an automatic image analysis method that was developed for spiral computed tomography scans (SCTS), with the objective of calculating the volume of muscle in the hind leg (HLMV CT ) and lumbar region (LRMV CT ) in lambs. The first step in the image analysis method was the isolation (segmentation) of the muscle regions in each image of the SCTS, using a new program that was implemented in the Sheep Tomogram Analysis Routines software (STAR). Due to the differences of muscle shape in the regions investigated, the new segmentation program applies different segmentation paths in specific subregions. These were automatically identified by the program based on skeletal landmarks. After the segmentation was completed, the muscles areas were automatically measured by counting the pixels representing muscle in each image; the volumes were calculated by adding the muscle areas of each image multiplied by the depth of the image (inter-slice distance). The accuracy of these measures of muscle volume was evaluated, using regression analysis, by comparing HLMV CT and LRMV CT to the hind leg and lumbar region muscle weights measured after dissection (HLMW D , no. ¼ 240, and LRMW D , no. ¼ 50, respectively) of Texel (TEX) and Scottish Blackface (SBF) female and male lambs slaughtered in 2003-04. The effects of breed, sex and year on the association (SCTS v. dissection) were evaluated. There was a strong association between HLMV CT and HLMW D ( R 2 ¼ 97·4%), which only increased slightly ( R 2 ¼ 97·7%) when breed was included in the model. This indicates that HLMW D can be estimated directly from HLMV CT with a high degree of accuracy. For the lumbar region, the association was high ( R 2 ¼ 83·0% to 88·8% depending on the model) but lower than in the hind leg, probably because the automatic segmentation isolates only the areas of the longissimus lumborum and multifidi muscles. Breed had a significant effect on the prediction of LRMW D from LRMV CT , as well as sex in the case of the TEX lambs. The results indicated that the predictions of LRMW D from LRMV CT require different equations for very divergent breeds such as TEX and SBF.

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Estimators of Tissue Proportions from X-Ray CT Images

Cited by 24 publications

References 14 publications

Development of a computed tomographic calibration method for the determination of lean meat content in pig carcasses

Development of a computed tomographic calibration method for the determination of lean meat content in pig carcasses

An Application of Image Processing Techniques in Computed Tomography Image Analysis

In vivo measurements of muscle volume by automatic image analysis of spiral computed tomography scans

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