Image preprocessing method and system for reducing the interference features of the bone age evaluation model

Abstract

The invention provides an image preprocessing method for reducing the interference features of the bone age evaluation model. The method comprises the following steps: 1) a hand region is detected; 2)a hand-type image is segmented; 3) palm angle is adjusted. The invention further provides an image preprocessing system for reducing interference features of the bone age evaluating model, comprisinga hand region detection module, a hand-shaped image segmentation module and a palm angle adjusting module, wherein the hand region detection module is used for positioning a hand bone, and removing interference features formed by different positions of the hand region in the image; the hand-shaped image segmentation module is used for the unification of segmentation and background of the hand bone, and the interference formed by different backgrounds is removed; the palm angle adjusting module is used for unifying the angle of the hand bone and removing interference formed by different angles. According to the invention, the position and the angle of the hand bone are adjusted through a preprocessing means, and the background is removed; the purpose of data amplification is achieved in amode of reducing interference feature, so that the model training is more effective, the classification capability and the robustness are good.

Description

technical field [0001] The invention relates to the field of bone age evaluation, and is used for preprocessing hand bone X-ray images before training a neural network model, so that the training is more efficient and the model effect is better. Background technique [0002] Bone age is one of the important indicators in the detection of physical development of children and adolescents, the inspection of endocrine diseases, and the selection of sports materials, so bone age evaluation has a wide range of applications. At present, bone age evaluation methods include manual methods, computer-aided methods, and deep learning methods. Due to the complexity of bone age assessment and the excessive human subjective factors, the first two methods cannot accurately assess bone age; with the development of deep learning technology, more and more researchers have begun to use deep learning for bone age assessment. . [0003] Existing deep learning models are mostly data-driven and r...

AI Technical Summary

Problems solved by technology

[0004] In order to overcome the shortcomings of the existing bone age evaluation methods, such as uneven bone age X-ray data quality, many interference features, difficult training of deep learning models, and poor classification ability and robustness, the present invention provides a method for reducing interference features. Image preprocessing method and system for bone age evaluation model with better classification ability and robustness to reduce interference features

Images