Texture Analysis for the Bone Age Assessment from MRI Images of Adolescent Wrists in Boys

Abstract

Currently, bone age is assessed by X-rays. It enables the evaluation of the child’s development and is an important diagnostic factor. However, it is not sufficient to diagnose a specific disease because the diagnoses and prognoses may arise depending on how much the given case differs from the norms of bone age. Background: The use of magnetic resonance images (MRI) to assess the age of the patient would extend diagnostic possibilities. The bone age test could then become a routine screening test. Changing the method of determining the bone age would also prevent the patient from taking a dose of ionizing radiation, making the test less invasive. Methods: The regions of interest containing the wrist area and the epiphyses of the radius are marked on the magnetic resonance imaging of the non-dominant hand of boys aged 9 to 17 years. Textural features are computed for these regions, as it is assumed that the texture of the wrist image contains information about bone age. Results: The regression analysis revealed that there is a high correlation between the bone age of a patient and the MRI-derived textural features derived from MRI. For DICOM T1-weighted data, the best scores reached 0.94 R2, 0.46 RMSE, 0.21 MSE, and 0.33 MAE. Conclusions: The experiments performed have shown that using the MRI images gives reliable results in the assessment of bone age while not exposing the patient to ionizing radiation.

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

“…Optional research may be conducted on bone corticalization [60]. This is a relatively new approach as, to the best of our knowledge, there are no similar approaches presented, excluding the work of our team [61] and the work of Dieckmayer [62], who evaluated age among different parameters with the use of deep learning methods; therefore, the approach was very different. Existing works where textures are used for bone assessment are focused on the evaluation of the presence of osteoporosis [63,64].…”

“…The meaning of texture feature abbreviations used in the article: e.g., YM4GlcmH4Contrast #1 {Y}: Y channel of YCbCr-luminance (also for grayscale images) #2 {D, M, S}: method of normalization-D (no normalization), M-(min-max normalization), S-normalization to <µ − 3σ, µ + 3σ> #3 {8, 7, 6, 5, 4}-number of depth bits (after quantization) #4 {DwtHaar, Gab, Glcm, Grlm, Hog, Lbp}-texture name #5 parameters of textures (direction, length} #6 statistical function-described in detail in [61].…”

Sex Differentiation of Trabecular Bone Structure Based on Textural Analysis of Pelvic Radiographs

“…The set of parameters was derived from various statistical image descriptors and comprised the following groups (as described in refs. [21,26]):…”

“…The set of parameters was derived from various statistical image descriptors and comprised the following groups (as described in refs. [21,26]):•First-order features (FOFs) are based on simple statistical information that can be determined from an image histogram. These features include the 10th percentile, 90th percentile, energy, total energy, entropy, minimum, maximum, mean, median, range, inter-quartile range, mean absolute deviation, robust mean absolute deviation, root mean squared, kurtosis, skewness, standard deviation, uniformity, and variance.• The gray-level co-occurrence matrix (GLCM) derives textural information by considering the spatial distribution of pixel brightness in the image.…”

Advances in Musculoskeletal Imaging and Their Applications

“…Another paper [13] considers the bone age evaluation of adolescent wrists in boys from MRI Images. Such a bone age is typically X-ray assessed.…”