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

Jeong, Hyun‐Dam; Kim, Jungmin; Ishida, Takayuki; Akiyama, Mitoshi; Kim, Y.

doi:10.1259/bjr.20101115

Cited by 8 publications

(3 citation statements)

References 21 publications

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“…Although, for many years, the reference for diagnostic imaging of bone in humans was the interpretation of a radiologist [25], who is a specialist in diagnostic imaging, the ability of the observer to detect image details does not necessarily reflect the amount of information present in the image [24]. Therefore, many attempts have been made to evaluate bone tissue structure, which is especially susceptible to textural analysis [39]. The idea of using textural analysis for the evaluation of human tissue visualized in diagnostic imaging techniques is not new.…”

Section: Discussionmentioning

confidence: 99%

Correlation of Bone Textural Parameters with Age in the Context of Orthopedic X-ray Studies

et al. 2023

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The aim of this study was to establish a relationship between the textural parameters observed in X-ray images of bones and the age of the individual. The study utilized a meticulous visual analysis of the images to identify significant correlations between textural features and age. Five distinct regions of interest, namely the Wing of the Ilium, Neck of the Femur, Greater Trochanter, Ischium, and Shaft of the Femur, were identified on both sides of the body. Textural parameters were then measured for each of these regions. The left femoral neck showed the most noteworthy associations, with the textures generated from the histogram of oriented gradients and gray-level co-occurrence matrix exhibiting the strongest correlations (ρ −0.52, p-value 4.95 × 10−14). The main finding of the current study is that correlation of age-dependent bone structure differences in the femoral neck area is higher than in other structures of the femur. This proposed methodology has the potential to aid in the early detection of osteoporosis, which is crucial for devising treatment plans and identifying potential risks associated with bone fragility.

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Section: Discussionmentioning

confidence: 99%

Correlation of Bone Textural Parameters with Age in the Context of Orthopedic X-ray Studies

et al. 2023

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“…As a new technology, radiomics can produce many features that reflect the microstructure of images. Some studies have shown that there is a certain correlation between the texture parameters of cancellous bone extracted from radiological images and bone microstructure [ 19 , 20 ]. He et al have established a radiomics model based on MRI for the diagnosis of osteoporosis [ 21 ].…”

Section: Discussionmentioning

confidence: 99%

Development and validation of a machine learning-derived radiomics model for diagnosis of osteoporosis and osteopenia using quantitative computed tomography

Xie

Chen

et al. 2022

BMC Med Imaging

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Background To develop and validate a quantitative computed tomography (QCT) based radiomics model for discriminating osteoporosis and osteopenia. Methods A total of 635 patients underwent QCT were retrospectively included from November 2016 to November 2019. The patients with osteopenia or osteoporosis (N = 590) were divided into a training cohort (N = 414) and a test cohort (N = 176). Radiomics features were extracted from the QCT images of the third lumbar vertebra. Minimum redundancy and maximum relevance and least absolute shrinkage and selection operator were used for data dimensional reduction, features selection and radiomics model building. Multivariable logistic regression was applied to construct the combined clinical-radiomic model that incorporated radiomics signatures and clinical characteristics. The performance of the combined clinical-radiomic model was evaluated by the area under the curve of receiver operator characteristic curve (ROC–AUC), accuracy, specificity, sensitivity, positive predictive value, and negative predictive value. Results The patients with osteopenia or osteoporosis were randomly divided into training and test cohort with a ratio of 7:3. Six more predictive radiomics signatures, age, alkaline phosphatase and homocysteine were selected to construct the combined clinical-radiomic model for diagnosis of osteoporosis and osteopenia. The AUC of the combined clinical-radiomic model was 0.96 (95% confidence interval (CI), 0.95 to 0.98) in the training cohort and 0.96 (95% CI 0.92 to 1.00) in the test cohort, which were superior to the clinical model alone (training-AUC = 0.81, test-AUC = 0.79). The calibration curve demonstrated that the radiomics nomogram had good agreement between prediction and observation and decision curve analysis confirmed clinically useful. Conclusions The combined clinical-radiomic model that incorporates the radiomics score and clinical risk factors, can serve as a reliable and powerful tool for discriminating osteoporosis and osteopenia.

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“…Textural features are used to parametrize the spatial patterns of an image responsible for its visual appearance as brightness coarseness, smoothness, and regularity, where most of these appearances are perceived by human senses [17]. Since the implementation of the MR analysis grey-level co-occurrence matrix by Haralick et al [18], magnetic resonance image textures have been used for the analysis of medical imaging in certain cases, such as cerebral tissue [19–23], liver [24], breast [19], bone marrow [25], muscle [26], fat tissue [27], bone, the prostate for automated cancer detection [28], and colorectal cancer monitoring [29, 30]. The quality of diffusion tensor imaging (DTI) was also parametrized [31, 32].…”

Section: Introductionmentioning

confidence: 99%

Influence of Acquisition Time on MR Image Quality Estimated with Nonparametric Measures Based on Texture Features

Obuchowicz

Piórkowski

Urbanik

et al. 2019

BioMed Research International

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Correlation of parametrized image texture features (ITF) analyses conducted in different regions of interest (ROIs) overcomes limitations and reliably reflects image quality. The aim of this study is to propose a nonparametrical method and classify the quality of a magnetic resonance (MR) image that has undergone controlled degradation by using textural features in the image. Images of 41 patients, 17 women and 24 men, aged between 23 and 56 years were analyzed. T2-weighted sagittal sequences of the lumbar spine, cervical spine, and knee and T2-weighted coronal sequences of the shoulder and wrist were generated. The implementation of parallel imaging with the use of GRAPPA2, GRAPPA3, and GRAPPA4 led to a substantial reduction in the scanning time but also degraded image quality. The number of degraded image textural features was correlated with the scanning time. Longer scan times correlated with markedly higher ITF image persistence in comparison with images computed with reduced scan times. Higher ITF preservation was observed in images of bones in the spine and femur as compared to images of soft tissues, i.e., tendons and muscles. Finally, a nonparametrized image quality assessment based on an analysis of the ITF, computed for different tissues, correlating with the changes in acquisition time of the MR images, was successfully developed. The correlation between acquisition time and the number of reproducible features present in an MR image was found to yield the necessary assumptions to calculate the quality index.

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

Cited by 8 publications

References 21 publications

Correlation of Bone Textural Parameters with Age in the Context of Orthopedic X-ray Studies

Correlation of Bone Textural Parameters with Age in the Context of Orthopedic X-ray Studies

Development and validation of a machine learning-derived radiomics model for diagnosis of osteoporosis and osteopenia using quantitative computed tomography

Influence of Acquisition Time on MR Image Quality Estimated with Nonparametric Measures Based on Texture Features

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