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首页> 外文会议>International work-conference on artificial neural networks >An Expert System Based on Using Artificial Neural Network and Region-Based Image Processing to Recognition Substantia Nigra and Atherosclerotic Plaques in B-Images: A Prospective Study
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An Expert System Based on Using Artificial Neural Network and Region-Based Image Processing to Recognition Substantia Nigra and Atherosclerotic Plaques in B-Images: A Prospective Study

机译：基于人工神经网络和区域图像处理的B图像黑质和动脉粥样硬化斑块识别专家系统的前瞻性研究

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摘要

The presented paper is focused on ways of digital image analysis of ultrasound B-images based on echogenicity investigation in determined Region of Interest (ROI). An expert system has been developed in the course of the research. The goal of the paper is to demonstrate how to interconnect automatic finding of the position of the substantia nigra using Artificial Neural Network (ANN) with supervised learning and ROI-based image analysis. For substantia nigra is able to detect the position using ANN from B-image in transverse thalamic plane. From this is computed echogenicity index grade inside the ROI as parkinsonism feature. The methodology is well applicable for a set of images with the same resolution. The results have shown practical application of ANN learning in this case. The second part of the paper is focused on detection of atherosclerotic plaques. An experimental prospective study shown the using ANN can be highly time-consuming problem due to complexity of B-images. The plaques have no standardized shape and size in comparison with SN. To objective appraisal of using ANN to automatic finding atherosclerotic plaque in B-image we need a large set of images of normal and pathological state. Although it is very important using ANN, automatic detection in highly time-consuming problem for ANN training.
机译：本文着重于在确定的感兴趣区域（ROI）中基于回声调查研究超声B图像的数字图像的方法。在研究过程中开发了一个专家系统。本文的目的是演示如何使用人工神经网络（ANN）结合有监督的学习和基于ROI的图像分析，将黑质的位置自动发现相互关联。对于黑质，能够使用ANN从丘脑横向平面中的B图像检测位置。由此计算出作为帕金森病特征的ROI内部的回声指数等级。该方法非常适用于具有相同分辨率的一组图像。结果显示了在这种情况下的人工神经网络学习的实际应用。本文的第二部分着重于动脉粥样硬化斑块的检测。一项实验性前瞻性研究表明，由于B图像的复杂性，使用人工神经网络可能会非常耗时。与SN相比，斑块没有标准化的形状和大小。为了客观评估使用人工神经网络自动查找B图像中的动脉粥样硬化斑块，我们需要大量正常和病理状态的图像。尽管使用人工神经网络非常重要，但是自动检测在人工神经网络训练中非常耗时的问题中。

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