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An Automated System for Skeletal Maturity Assessment by Extreme Learning Machines.

Mansourvar M, Shamshirband S, Raj RG, Gunalan R, Mazinani I - PLoS ONE (2015)

Bottom Line: The estimation results of ELM models are compared with those of genetic programming (GP) and artificial neural networks (ANNs) models.The experimental results signify improvement in assessment accuracy over GP and ANN, while generalization capability is possible with the ELM approach.Moreover, the results are indicated that the ELM model developed can be used confidently in further work on formulating novel models of skeletal age assessment strategies.

View Article: PubMed Central - PubMed

Affiliation: Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur, 50603, Malaysia.

ABSTRACT
Assessing skeletal age is a subjective and tedious examination process. Hence, automated assessment methods have been developed to replace manual evaluation in medical applications. In this study, a new fully automated method based on content-based image retrieval and using extreme learning machines (ELM) is designed and adapted to assess skeletal maturity. The main novelty of this approach is it overcomes the segmentation problem as suffered by existing systems. The estimation results of ELM models are compared with those of genetic programming (GP) and artificial neural networks (ANNs) models. The experimental results signify improvement in assessment accuracy over GP and ANN, while generalization capability is possible with the ELM approach. Moreover, the results are indicated that the ELM model developed can be used confidently in further work on formulating novel models of skeletal age assessment strategies. According to the experimental results, the new presented method has the capacity to learn many hundreds of times faster than traditional learning methods and it has sufficient overall performance in many aspects. It has conclusively been found that applying ELM is particularly promising as an alternative method for evaluating skeletal age.

No MeSH data available.


Related in: MedlinePlus

CBIR layout in the bone age assessment system.
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pone.0138493.g001: CBIR layout in the bone age assessment system.

Mentions: The Content-based image retrieval (CBIR) approach is become famous in medical imaging as well as crime prevention in recent years [31]. The CBIR system was developed in the 1990s to solve problems encountered in text-based image retrieval. The CBIR method is based on querying by image [32]. Content-based image retrieval is a robust method to determine age independent of bone measurements. The CBIR methodology for skeletal age assessment is involves comparing image content for a new input with earlier samples. Most BAA systems are applied to the regions of interest (ROIs) in hand bones, which leads to low accuracy in bone age assessment [17,33,34]. The new method utilized in our study overcomes the mentioned limitation in literature by using complete images for an individual query instead of applying the query to the regions of interest (ROIs) [35]. The CBIR assessment methodology is found on compressing image content from a new sample with the earlier samples. Fig 1 shows the CBIR layout applied in our BAA system.


An Automated System for Skeletal Maturity Assessment by Extreme Learning Machines.

Mansourvar M, Shamshirband S, Raj RG, Gunalan R, Mazinani I - PLoS ONE (2015)

CBIR layout in the bone age assessment system.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4581666&req=5

pone.0138493.g001: CBIR layout in the bone age assessment system.
Mentions: The Content-based image retrieval (CBIR) approach is become famous in medical imaging as well as crime prevention in recent years [31]. The CBIR system was developed in the 1990s to solve problems encountered in text-based image retrieval. The CBIR method is based on querying by image [32]. Content-based image retrieval is a robust method to determine age independent of bone measurements. The CBIR methodology for skeletal age assessment is involves comparing image content for a new input with earlier samples. Most BAA systems are applied to the regions of interest (ROIs) in hand bones, which leads to low accuracy in bone age assessment [17,33,34]. The new method utilized in our study overcomes the mentioned limitation in literature by using complete images for an individual query instead of applying the query to the regions of interest (ROIs) [35]. The CBIR assessment methodology is found on compressing image content from a new sample with the earlier samples. Fig 1 shows the CBIR layout applied in our BAA system.

Bottom Line: The estimation results of ELM models are compared with those of genetic programming (GP) and artificial neural networks (ANNs) models.The experimental results signify improvement in assessment accuracy over GP and ANN, while generalization capability is possible with the ELM approach.Moreover, the results are indicated that the ELM model developed can be used confidently in further work on formulating novel models of skeletal age assessment strategies.

View Article: PubMed Central - PubMed

Affiliation: Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur, 50603, Malaysia.

ABSTRACT
Assessing skeletal age is a subjective and tedious examination process. Hence, automated assessment methods have been developed to replace manual evaluation in medical applications. In this study, a new fully automated method based on content-based image retrieval and using extreme learning machines (ELM) is designed and adapted to assess skeletal maturity. The main novelty of this approach is it overcomes the segmentation problem as suffered by existing systems. The estimation results of ELM models are compared with those of genetic programming (GP) and artificial neural networks (ANNs) models. The experimental results signify improvement in assessment accuracy over GP and ANN, while generalization capability is possible with the ELM approach. Moreover, the results are indicated that the ELM model developed can be used confidently in further work on formulating novel models of skeletal age assessment strategies. According to the experimental results, the new presented method has the capacity to learn many hundreds of times faster than traditional learning methods and it has sufficient overall performance in many aspects. It has conclusively been found that applying ELM is particularly promising as an alternative method for evaluating skeletal age.

No MeSH data available.


Related in: MedlinePlus