Computer-assisted bone age assessment: image preprocessing and epiphyseal/metaphyseal ROI extraction

Piętka, Ewa; Gertych, Arkadiusz; Pospiech, S.; Cao, Fei; Huang, H. K.; Gilsanz, Vicente

doi:10.1109/42.938240

Cited by 190 publications

(92 citation statements)

References 26 publications

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“…Previous work by Pietka, Gertych, Pospiech-Kurkowska, et al [10] used the Phalanges as the ROI for feature extraction. The dynamic changes in the Phalanges in children between the ages of 7 to 16 (male) or 14 (female) years of age provided information for bone maturity assessment within this age range.…”

Section: Previous Workmentioning

confidence: 99%

Automated bone age assessment of older children using the radius

et al. 2008

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The Digital Hand Atlas in Assessment of Skeletal Development is a large-scale Computer Aided Diagnosis (CAD) project for automating the process of grading Skeletal Development of children from 0-18 years of age. It includes a complete collection of 1,400 normal hand X-rays of children between the ages of 0-18 years of age. Bone Age Assessment is used as an index of skeletal development for detection of growth pathologies that can be related to endocrine, malnutrition and other disease types. Previous work at the Image Processing and Informatics Lab (IPILab) allowed the bone age CAD algorithm to accurately assess bone age of children from 1 to 16 (male) or 14 (female) years of age using the Phalanges as well as the Carpal Bones. At the older ages (16(male) or 14(female) −19 years of age) the Phalanges as well as the Carpal Bones are fully developed and do not provide well-defined features for accurate bone age assessment. Therefore integration of the Radius Bone as a region of interest (ROI) is greatly needed and will significantly improve the ability to accurately assess the bone age of older children. Preliminary studies show that an integrated Bone Age CAD that utilizes the Phalanges, Carpal Bones and Radius forms a robust method for automatic bone age assessment throughout the entire age range (1-19 years of age).

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Section: Previous Workmentioning

confidence: 99%

Automated bone age assessment of older children using the radius

et al. 2008

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“…al. [48] in the year 2000. In this, images were rotated for specified direction if required and the background pixels are removed using a dynamic threshold method.…”

Section: Dynamic Threshold Methodmentioning

confidence: 99%

Automated Human Bone Age Assessment using Image Processing Methods - Survey

Rajitha¹,

Agarwal²

2014

IJCA

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“…'Background' refers to the area outside the radiation field caused by the blocking of the collimator and resulting in white borders surrounding the radiation field. Its removal is important, because it reduces the amount of unwanted light in images as well as almost transparent borders on the film during the soft copy display [34]. For this purpose, we used a morphological opening operation to estimate the background.…”

Section: Feature Extraction Using Image Processingmentioning

confidence: 99%

Bone age determination in young children (newborn to 6 years old) using support vector machines

Güraksın¹,

Uğuz²,

Baykan³

2016

Turk J Elec Eng & Comp Sci

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Abstract:Bone age is assessed through a radiological analysis of the left-hand wrist and is then compared to chronological age. A conflict between these two values indicates an abnormality in the development process of the skeleton. This study, conducted on children aged between 0 and 6 years, proposes a computer-based diagnostic system to eliminate the disadvantages of the methods used in bone age determination. For this purpose, primarily an image processing procedure was applied to the X-ray images of the left-hand wrist of children from different ethnic groups aged between 0 and 6 years. A total of 9 features, corresponding to the carpal bones and distal epiphysis of the radius bone with some physiological attributes of the children, were obtained. Then, by using gain ratio, the best 6 features were used for the classification process. Next, the bone age determination process was performed with the obtained features with the help of the support vector machine (SVM), naive Bayes, k-nearest neighborhood, and C4.5 algorithms. Finally, the features used in the determination process and their effects on the accuracies were examined. The results of the designed system showed that SVM method has a better achievement rate than the other methods at a rate of 72.82%. Additionally, in this study, a new feature corresponding to the distance between the centers of gravity of the carpal bones was used for the classification process, and the analysis of the related feature showed that there was a statistically significant difference at P <0.05 between this feature and bones in children aged between 0 and 6 years.

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Computer-assisted bone age assessment: image preprocessing and epiphyseal/metaphyseal ROI extraction

Cited by 190 publications

References 26 publications

Automated bone age assessment of older children using the radius

Automated bone age assessment of older children using the radius

Automated Human Bone Age Assessment using Image Processing Methods - Survey

Bone age determination in young children (newborn to 6 years old) using support vector machines

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