A Deep Convolutional Architectural Framework for Radiograph Image Processing at Bit Plane Level for Gender & Age Assessment
A Deep Convolutional Architectural Framework for Radiograph Image Processing at Bit Plane Level for Gender & Age Assessment
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DOI：
10.32604/cmc.2020.08552
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发表时间：
2020
期刊：
影响因子：
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通讯作者：
N. Rani;M. Chandrajith;R. PushpaB;J. BipinNairB
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文献类型：
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作者：
N. Rani;M. Chandrajith;R. PushpaB;J. BipinNairB
通过骨骼来评估一个人的年龄是一种真正确定个人技能的傻瓜证明方法。在过去的几次尝试中，报告了一个人的实足年龄的评估的基础上发现在手腕X光片图像的各种歧视性的功能。这些特征的排列和组合实现了令人满意的精度为一组有限的群体。在本文中，性别的个人的实足年龄在1-17岁之间的评估是使用左手腕部X光片图像。提出了一种全自动的方法来去除由于在射线图像采集过程中的非均匀照明而持续存在的噪声。在此基础上，提出了一种基于位平面运算的手腕区域提取方法。应用称为深度卷积神经网络的GeNet的框架将提取的手腕区域分类为男性和女性。实验在北美放射学会（RSNA）的约12442幅图像的数据集上进行。预处理和分割技术的效率导致约99.09%的相关性。GeNet的性能进行了评估，提取的手腕区域，导致准确率为82.18%。
: Assessing the age of an individual via bones serves as a fool proof method in true determination of individual skills. Several attempts are reported in the past for assessment of chronological age of an individual based on variety of discriminative features found in wrist radiograph images. The permutation and combination of these features realized satisfactory accuracies for a set of limited groups. In this paper, assessment of gender for individuals of chronological age between 1-17 years is performed using left hand wrist radiograph images. A fully automated approach is proposed for removal of noise persisted due to non-uniform illumination during the process of radiograph acquisition process. Subsequent to this a computational technique for extraction of wrist region is proposed using operations on specific bit planes of image. A framework called GeNet of deep convolutional neural network is applied for classification of extracted wrist regions into male and female. The experimentations are conducted on the datasets of Radiological Society of North America (RSNA) of about 12442 images. Efficiency of preprocessing and segmentation techniques resulted into a correlation of about 99.09%. Performance of GeNet is evaluated on the extracted wrist regions resulting into an accuracy of 82.18%.