Computerized radiographic analysis of osteoporosis: preliminary evaluation.

Caligiuri, Philip; Giger, Maryellen L.; Favus, Murray J.; Jia, Hanxiang; Doi, Kunio; Dixon, Larry

doi:10.1148/radiology.186.2.8421753

Cited by 109 publications

(76 citation statements)

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“…According to Katsuragawa et al [19], Caligiuri et al [21] and this study, the collapse of the microtrabecular pattern caused the increase in RMS. As a result, RMS, FMP and fractal dimension were shown as high values (Figures 12-15).…”

Section: Discussionsupporting

confidence: 56%

“…Fast Fourier transformation was then performed, and the resulting power spectrum was analysed in order to yield the RMS [21]:…”

Section: Processing For Regions Of Interestmentioning

confidence: 99%

“…where f(m,n) corresponds to the Fourier transform for the background-corrected ROI of the trabecular pattern, and m and n are indices of the ROI array [21].…”

Section: Processing For Regions Of Interestmentioning

confidence: 99%

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Computerised analysis of osteoporotic bone patterns using texture parameters characterising bone architecture

Jeong¹,

Kim²,

Ishida³

et al. 2013

BJR

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Objective: To evaluate the geometric change of osteoporotic bone trabecular patterns using root mean square (RMS) values, first moment power spectrum (FMP) values and fractal dimension values. With the use of these methods, we attempted computerised analysis of osteoporotic bone patterns using texture parameters characterising bone architecture and computer-aided diagnosis of osteoporosis. Methods: 32 patient cases from Korea University Guro Hospital were analysed. Patient ages ranged from 51 to 89 years, with a mean age of 65 years. Receiver operating characteristic curve analysis was performed with determination of the area under the curve (AUC). Results: The bone mineral density (BMD) measurement (AUC50.78) was a better indicator of bone quantity than the RMS, FMP and fractal dimension values (AUC50.72) for diagnosis; therefore the combination of RMS, FMP and fractal dimension values was a better indicator of bone quality. Conclusion: Measurements that combined BMD measurement and RMS values and combined FMP and fractal dimension values (AUC50.85) together produced better results than the use of the two parameter sets separately for a diagnosis of osteoporosis. Advances in knowledge: For more effective application, additional study on more cases and data will be required.

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Section: Discussionsupporting

confidence: 56%

“…Fast Fourier transformation was then performed, and the resulting power spectrum was analysed in order to yield the RMS [21]:…”

Section: Processing For Regions Of Interestmentioning

confidence: 99%

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Computerised analysis of osteoporotic bone patterns using texture parameters characterising bone architecture

Jeong¹,

Kim²,

Ishida³

et al. 2013

BJR

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“…B Nonskeletal element. Bg, background only with no object; Ph, a 10-mm-thick soft tissue phantom; Bn, an excised human femoral bone block measuring 10 mm 3 receives in the popular press as well as in professional research journals [2][3][4][5][6][7][8]22]. Osteoporosis is defined as a generalized bone disorder characterized by a decrease in bone mass and deterioration of bone microstructure, which results in decreased bone strength and the possibility of fracture [22].…”

Section: Discussionmentioning

confidence: 99%

“…Bone strength is evaluated primarily by measurement of the bone mass [2,3]. On the other hand, bone morphometric methods, such as plain X-ray images, and bone texture analysis using computed tomography (CT) and magnetic resonance imaging (MRI) are available for evaluation of bone structures as well [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

Quantitative analysis using the star volume method applied to skeleton patterns extracted with a morphological filter

2000

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In this study, a morphological filter was combined with star volume analysis and applied to digital images to determine its potential usefulness in assessing trabecular structure. Three digital "geometric" test patterns (square, rectangle, circle) were created on a CRT (cathode ray tube). Each shape was arranged into five groups by size to yield 15 final "skeletal" patterns that were subsequently assessed with star volume analysis. Also, three digital X-ray images (background, soft tissue, bone block) were processed with a morphological filter to create three sets of 11 skeletal patterns each. These patterns were also assessed with star volume analysis and the ratio of extracted skeletal elements (in pixel numbers) to total pixel numbers was expressed as the pixel percentage. Star volume analysis was then applied to these digital skeletal images to yield the volume of extracted "skeletal" trabecular elements (Vsk) and the volume of nonskeletal (marrow space) elements (Vsp). The Vsk and Vsp were compared for all the different skeletal patterns. The pixel percentages were then compared to the star volume results for the X-ray test patterns. The Vsk decreased and Vsp increased as the number of operations (n) increased for both digital X-ray images and the geometric test patterns when the X-ray images were depicted by pixel percentages. Also, all true bone test patterns were clearly different both visually and quantitatively when compared to the noise skeletons extracted from background and soft tissue. Therefore, as Vsk was increased, so was connectivity. It can be concluded that the application of morphological filters and star volume analysis may be a useful tool in quantitatively determining the characteristics and continuity of trabecular skeletal structures. Further studies involving a larger number of bone samples and using models to compare measurements of calculated versus actual volume should reveal the true potential of this method for evaluating bone structure and its relationship to bone strength and also increase the tools available for evaluating bone diseases such as osteoporosis.

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Trabecular Bone Score: A Noninvasive Analytical Method Based Upon the DXA Image

Silva

Leslie

Resch³

et al. 2014

Journal of Bone and Mineral Research

706

487

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The trabecular bone score (TBS) is a gray-level textural metric that can be extracted from the two-dimensional lumbar spine dualenergy X-ray absorptiometry (DXA) image. TBS is related to bone microarchitecture and provides skeletal information that is not captured from the standard bone mineral density (BMD) measurement. Based on experimental variograms of the projected DXA image, TBS has the potential to discern differences between DXA scans that show similar BMD measurements. An elevated TBS value correlates with better skeletal microstructure; a low TBS value correlates with weaker skeletal microstructure. Lumbar spine TBS has been evaluated in cross-sectional and longitudinal studies. The following conclusions are based upon publications reviewed in this article: 1) TBS gives lower values in postmenopausal women and in men with previous fragility fractures than their nonfractured counterparts; 2) TBS is complementary to data available by lumbar spine DXA measurements; 3) TBS results are lower in women who have sustained a fragility fracture but in whom DXA does not indicate osteoporosis or even osteopenia; 4) TBS predicts fracture risk as well as lumbar spine BMD measurements in postmenopausal women; 5) efficacious therapies for osteoporosis differ in the extent to which they influence the TBS; 6) TBS is associated with fracture risk in individuals with conditions related to reduced bone mass or bone quality. Based on these data, lumbar spine TBS holds promise as an emerging technology that could well become a valuable clinical tool in the diagnosis of osteoporosis and in fracture risk assessment.

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Computerized radiographic analysis of osteoporosis: preliminary evaluation.

Cited by 109 publications

References 0 publications

Computerised analysis of osteoporotic bone patterns using texture parameters characterising bone architecture

Computerised analysis of osteoporotic bone patterns using texture parameters characterising bone architecture

Quantitative analysis using the star volume method applied to skeleton patterns extracted with a morphological filter

Trabecular Bone Score: A Noninvasive Analytical Method Based Upon the DXA Image

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