Preprocessing with Contrast Enhancement Methods in Bone Age Assessment

Aung, Aye Aye; Win, Zin Mar

doi:10.1007/978-3-030-25213-7_3

Cited by 2 publications

(2 citation statements)

References 18 publications

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“…After automatic removal of the guidance lines penetrating ROIs by substituting line pixel values with median values of an adjacent 3‐by‐3 pixel region using our in‐house python code, contrast‐limited adaptive histogram equalization followed by median filtering was performed to enhance texture patterns in DXA hip images with reduction of background noise. ( 15,16 )…”

Section: Methodsmentioning

confidence: 99%

Bone Radiomics Score Derived From DXA Hip Images Enhances Hip Fracture Prediction in Older Women

Hong

Park

Kim

et al. 2020

Journal of Bone and Mineral Research

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Dual-energy X-ray absorptiometry (DXA)-based bone mineral density testing is standard to diagnose osteoporosis to detect individuals at high risk of fracture. A radiomics approach to extract quantifiable texture features from DXA hip images may improve hip fracture prediction without additional costs. Here, we investigated whether bone radiomics scores from DXA hip images could improve hip fracture prediction in a community-based cohort of older women. The derivation set (143 women who sustained hip fracture [mean age 73 years, time to fracture median 2.1 years] versus 290 age-matched women [mean age 73 years] who did not sustain hip fracture during follow-up [median 5.5 years]) were split into the train set (75%) and the test set (25% hold-out set). Among various models using 14 selected features out of 300 texture features mined from DXA hip images in the train set, random forest model was selected as the best model to build a bone radiomics score (range 0 to 100) based on the performance in the test set. In a communitybased cohort (2029 women, mean age 71 years) as the clinical validation set, the bone radiomics score was calculated using a model fitted in the train set. A total of 34 participants (1.7%) sustained hip fracture during median follow-up of 5.4 years (mean bone radiomics score 40 AE 16 versus 28 AE 12 in non-fractured, p < 0.001). A one-point bone radiomics score increment was associated with a 4% elevated risk of incident hip fracture (adjusted hazard ratio [aHR] = 1.04, p = 0.001) after adjustment for age, body mass index (BMI), previous history of fracture, and femoral neck T-score, with improved model fit when added to covariates (likelihood ratio chi-square 10.74, p = 0.001). The association between bone radiomics score with incident hip fracture remained robust (aHR = 1.06, p < 0.001) after adjustment for FRAX hip fracture probability. Bone radiomics scores estimated from texture features of DXA hip images have the potential to improve hip fracture prediction.

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Section: Methodsmentioning

confidence: 99%

Bone Radiomics Score Derived From DXA Hip Images Enhances Hip Fracture Prediction in Older Women

Hong

Park

Kim

et al. 2020

Journal of Bone and Mineral Research

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“…CLAHE works on small regions of the image, enabling it to adaptively enhance contrast based on local variations. This characteristic ensures that fine details in the X-ray images, such as the cortical boundaries of the metacarpal bones, are preserved and enhanced, leading to improved model performance in detecting and segmenting these regions [31,32]. Subsequently, in order to rectify the inconsistency in the output X-ray images due to variations in the positioning of patients' wrists during X-ray capture, a vertical alignment classification approach was employed for metacarpal bone standardization in the X-ray images.…”

Section: Data Preprocessing and Augmentationmentioning

confidence: 99%

An Automated Assessment Method for Chronic Kidney Disease–Mineral and Bone Disorder (CKD-MBD) Utilizing Metacarpal Cortical Percentage

Wu,

Tseng,

Gau

et al. 2024

Electronics

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Chronic kidney disease–mineral and bone disorder (CKD-MBD) frequently occurs in hemodialysis patients and is a common cause of osteoporosis. Regular dual-energy X-ray absorptiometry (DXA) scans are used to monitor these patients, but frequent, cost-effective, and low-dose alternatives are needed. This study proposes an automatic CKD-MBD assessment model using histogram equalization and a squeeze-and-excitation block-based residual U-Net (SER-U-Net) with hand diagnostic radiography for preliminary classification. The process involves enhancing image contrast with histogram equalization, extracting features with the SE-ResNet model, and segmenting metacarpal bones using U-Net. Ultimately, a correlation analysis is carried out between the calculated dual metacarpal cortical percentage (dMCP) and DXA T-scores. The model’s performance was validated by analyzing clinical data from 30 individuals, achieving a 93.33% accuracy in classifying bone density compared to DXA results. This automated method provides a rapid, effective tool for CKD-MBD assessment in clinical settings.

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Preprocessing with Contrast Enhancement Methods in Bone Age Assessment

Cited by 2 publications

References 18 publications

Bone Radiomics Score Derived From DXA Hip Images Enhances Hip Fracture Prediction in Older Women

Bone Radiomics Score Derived From DXA Hip Images Enhances Hip Fracture Prediction in Older Women

An Automated Assessment Method for Chronic Kidney Disease–Mineral and Bone Disorder (CKD-MBD) Utilizing Metacarpal Cortical Percentage

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