Automated bone age assessment using bag of features and random forests

Simu, Shreyas; Lal, Shyam

doi:10.1109/iss1.2017.8389311

Cited by 9 publications

(1 citation statement)

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“…Conversely, a systematic approach by Sheshasaayee and Jasmine [ 37 ] have used conflated Gabor, local binary pattern, and color histogram features to train their SVM regressor to predict the bone age more efficiently. In addition to that, Simu and Lal [ 38 ] have tested three different features that comprise of texture data, a histogram of oriented gradients (HOG), and a bag of features (BoF), which are extracted from the phalanges bone regions as an input to Random Forest predictor. Henceforth, selecting the right and suitable features to train the predictor or regressor is a crucial task in the conventional machine learning approach.…”

Section: Related Workmentioning

confidence: 99%

Intelligent Bone Age Assessment: An Automated System to Detect a Bone Growth Problem Using Convolutional Neural Networks with Attention Mechanism

et al. 2021

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Skeletal bone age assessment using X-ray images is a standard clinical procedure to detect any anomaly in bone growth among kids and babies. The assessed bone age indicates the actual level of growth, whereby a large discrepancy between the assessed and chronological age might point to a growth disorder. Hence, skeletal bone age assessment is used to screen the possibility of growth abnormalities, genetic problems, and endocrine disorders. Usually, the manual screening is assessed through X-ray images of the non-dominant hand using the Greulich–Pyle (GP) or Tanner–Whitehouse (TW) approach. The GP uses a standard hand atlas, which will be the reference point to predict the bone age of a patient, while the TW uses a scoring mechanism to assess the bone age using several regions of interest information. However, both approaches are heavily dependent on individual domain knowledge and expertise, which is prone to high bias in inter and intra-observer results. Hence, an automated bone age assessment system, which is referred to as Attention-Xception Network (AXNet) is proposed to automatically predict the bone age accurately. The proposed AXNet consists of two parts, which are image normalization and bone age regression modules. The image normalization module will transform each X-ray image into a standardized form so that the regressor network can be trained using better input images. This module will first extract the hand region from the background, which is then rotated to an upright position using the angle calculated from the four key-points of interest. Then, the masked and rotated hand image will be aligned such that it will be positioned in the middle of the image. Both of the masked and rotated images will be obtained through existing state-of-the-art deep learning methods. The last module will then predict the bone age through the Attention-Xception network that incorporates multiple layers of spatial-attention mechanism to emphasize the important features for more accurate bone age prediction. From the experimental results, the proposed AXNet achieves the lowest mean absolute error and mean squared error of 7.699 months and 108.869 months2, respectively. Therefore, the proposed AXNet has demonstrated its potential for practical clinical use with an error of less than one year to assist the experts or radiologists in evaluating the bone age objectively.

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Section: Related Workmentioning

confidence: 99%