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首页> 外文期刊>Chaos, Solitons and Fractals: Applications in Science and Engineering: An Interdisciplinary Journal of Nonlinear Science >Novel approaches to determine age and gender from dental x-ray images by using multiplayer perceptron neural networks and image processing techniques
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Novel approaches to determine age and gender from dental x-ray images by using multiplayer perceptron neural networks and image processing techniques

机译:通过使用多人游客来源的神经网络和图像处理技术来确定从牙科X射线图像中的年龄和性别的新方法

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摘要

It may be necessary to determine the identity or gender of a person for any reason (disasters, inheritance etc.). In such cases, forensic medical institutions are asked for help. Forensic science institutions try to estimate the age of people's teeth and bones. In this study, a novel algorithm was developed to keep these predictions at the highest level and to obtain definite results. The data base of 162 different tooth classes is created manually. All image sizes are 150x150 pixels. First, image preprocessing techniques have been applied to teeth images. These preprocessing techniques were first applied to teeth images. After this process, the segmentation process of the teeth images was performed to extract the feature by novel segmentation algorithm. Segmentation can be done automatically and dynamically. Numerical data obtained as a result of feature extraction from dental images is presented as an inputs to Multi layer perceptron neural network. In application, feature reduction can be performed. Thanks to the originally developed algorithm, the highest success rates were obtained with the highest 99.9% (full segment) and 100% (notfull segment) classification. After classification, for many dental groups the age estimate is performed with zero error. Application was developed as a multidisciplinary study. (C) 2019 Elsevier Ltd. All rights reserved.
机译:可能有必要以任何原因(灾难,遗产等)确定人的身份或性别。在这种情况下,要求法医医疗机构获得帮助。法医学科学机构试图估计人们的牙齿和骨骼的年龄。在这项研究中,开发了一种新颖的算法以保持最高水平的这些预测,并获得明确的结果。手动创建162个不同的牙齿类的数据库。所有图像大小都是150x150像素。首先,已将图像预处理技术应用于齿图像。首先将这些预处理技术应用于牙齿图像。在此过程之后,执行牙齿图像的分割过程以通过新的分割算法提取特征。可以自动且动态地完成分割。作为来自牙科图像的特征提取而获得的数值数据作为多层Perceptron神经网络的输入呈现。在应用中,可以执行特征减少。由于最初开发的算法,获得了最高的成功率,以最高的99.9%(全段)和100%(不满)分类。分类后,对于许多牙科群体,年龄估计以零误差进行。申请被制定为多学科研究。 (c)2019年elestvier有限公司保留所有权利。

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