Bone age estimation based on phalanx information with fuzzy constrain of carpals

Hsieh, Chia‐Wei; Jong, Tai-Lang; Tiu, Chui‐Mei

doi:10.1007/s11517-006-0155-9

Cited by 45 publications

(21 citation statements)

References 16 publications

(16 reference statements)

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“…It is therefore difficult to localize and segment the PROI and EMROI. The technology of cropping the left hand can be accomplished by the previous study [10].…”

Section: Crop the Left-hand Regionmentioning

confidence: 99%

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Automatic Segmentation of Phalanx and Epiphyseal/Metaphyseal Region by Gamma Parameter Enhancement Algorithm

Hsieh¹,

Chen²,

Jong³

et al. 2012

Measurement Science Review

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The performance of bone age assessment is highly correlated with the extraction of bony tissue from soft tissues, and the key problem is how to successfully separate epiphyseal/metaphyseal region of interests (EMROIs) from the background and soft tissue. In our experiment, a series of image preprocessing procedures are used to exclude the background and locate the EMROIs of lefthand radiographs. Subsequently, automatic gamma parameter enhancement is applied to test the two segmentation methods (adaptive two-means clustering algorithm and gradient vector flow snake) among children of different age (the age from 2 to 16 years for 80 girls and boys). Four error measurements of misclassification error, relative foreground area error, modified Hausdorff distances, and edge mismatch, are included to evaluate the segmentation performance. The result shows that the two segmentation algorithms are corresponding to different ranges of optimal gamma parameters. Furthermore, the margin of EMROIs can be obtained more precisely by developing an automatic bone age assessment method with the gamma parameter enhancement.

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“…It is therefore difficult to localize and segment the PROI and EMROI. The technology of cropping the left hand can be accomplished by the previous study [10].…”

Section: Crop the Left-hand Regionmentioning

confidence: 99%

“…The advantages of BAA include reproducibility and reliability; therefore, many reports focusing on the precise computerbased BAA have been made for improving the performance [6][7][8][9][10]. The critical point of making BAA is how to extract useful characteristics such as bone size and shape.…”

Section: Introductionmentioning

confidence: 99%

Automatic Segmentation of Phalanx and Epiphyseal/Metaphyseal Region by Gamma Parameter Enhancement Algorithm

Hsieh¹,

Chen²,

Jong³

et al. 2012

Measurement Science Review

View full text Add to dashboard Cite

show abstract

“…Difference in bone age and chronological age can indicate medical problems. 12,13 In clinical radiology, bone age assessment is a task regularly performed by pediatricians to monitor skeletal development and the effects of certain drugs. …”

Section: Bone Age Assessmentmentioning

confidence: 99%

On the Segmentation and Classification of Hand Radiographs

Davis

Theobald

Lines

et al. 2012

Int. J. Neur. Syst.

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This research is part of a wider project to build predictive models of bone age using hand radiograph images. We examine ways of finding the outline of a hand from an X-ray as the first stage in segmenting the image into constituent bones. We assess a variety of algorithms including contouring, which has not previously been used in this context. We introduce a novel ensemble algorithm for combining outlines using two voting schemes, a likelihood ratio test and dynamic time warping (DTW). Our goal is to minimize the human intervention required, hence we investigate alternative ways of training a classifier to determine whether an outline is in fact correct or not. We evaluate outlining and classification on a set of 1370 images. We conclude that ensembling with DTW improves performance of all outlining algorithms, that the contouring algorithm used with the DTW ensemble performs the best of those assessed, and that the most effective classifier of hand outlines assessed is a random forest applied to outlines transformed into principal components.

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“…The method Fig. 1 The flow chat of the study was illustrated based on morphology produced a rectangular CROI with four points A, B, C, and D. The procedure of the details was proposed in the articles [6][7][8], and the CROI was presented as shown in Fig. 2.…”

Section: Databasementioning

confidence: 99%

A fuzzy-based growth model with principle component analysis selection for carpal bone-age assessment

Hsieh

Liu

Jong

et al. 2010

Med Biol Eng Comput

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There are two well-known methods to assess bone age, the Greulich-Pyle method and the Tanner-Whitehouse method, which both utilize the hand radiogram to make bone-age assessment to assist medical doctors to identify the growth status of children. Basically, the morphology of bones could be evaluated to quantitatively describe the maturity. The study extracted the morphology of carpal bones and applied the fuzzy theory with principle component analysis to estimate the maturity of skeleton. Five geometric features of the carpals were extracted including the bone area, the area ratio, and the bone contour of the carpals. In order to analyze these features, the principle component analysis and the statistical correlation combined with three different types of procedure were used to construct a growth model of carpals. Eventually, the results of the three types of procedure with fuzzy rules can construct a bone-age assessment system to identify the maturity of children. The study shows that the proposed model based on fuzzy rule has an accuracy rate above 89% in Type-I and II, and above 87% in Type-III within a tolerance of 1.5 years.

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Bone age estimation based on phalanx information with fuzzy constrain of carpals

Cited by 45 publications

References 16 publications

Automatic Segmentation of Phalanx and Epiphyseal/Metaphyseal Region by Gamma Parameter Enhancement Algorithm

Automatic Segmentation of Phalanx and Epiphyseal/Metaphyseal Region by Gamma Parameter Enhancement Algorithm

On the Segmentation and Classification of Hand Radiographs

A fuzzy-based growth model with principle component analysis selection for carpal bone-age assessment

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