基于受限玻尔兹曼机的医学图像分类方法研究
发布时间：2018-09-11 10:05
【摘要】：随着计算机技术的发展,越来越多的医学图像分析技术也应运而生。利用数据挖掘方法对医学图像做分析是目前研究的热点之一,目前很多数据挖掘方法已经成功应用在了医学图像的分类中,但这些方法中的很大一部分都是先提取医学图像中的统计学特征,然后再在此特征数据集基础上对图像进行分析,从而实现对医学图像的诊断。目前这种通过特征提取来对医学图像进行分析的方法主要有关联规则、决策树、遗传算法、人工神经网络、贝叶斯网络、粗糙集、支持向量机等方法。但是基于统计特征提取的医学图像分析方法中特征提取的好坏直接影响着图像的分析结果,而且特征的选择会受到经验等主观因素的影响。目前比较流行的特征提取方法是深度学习方法,此方法利用深度信念网络(Deep Belief Network,DBN)模型对输入数据进行特征学习。DBN实际上是一个有向图模型,它的基础模型是无向图模型受限玻尔兹曼机(Restricted Boltzmann Machine,RBM),由于在多层有向图中,推断隐层单元的后验分布是相当困难的,于是DBN特征的学习采用每次学习一个RBM的方式。这是因为RBM二部图结构可以让对隐层单元的状态的推断变得很简单。每个RBM学习到的特征即其隐层单元的状态将会作为下一层RBM的输入数据,以此类推,完成DBN的训练。而且Hinton等人已经证明每增加一层RBM,DBN的训练数据的对数概率可变边界就会降低,即DBN对训练数据的表达能力就会更强。DBN因为其学习输入数据中复杂的高层次的特征结构的能力,已经得到了广泛的研究和应用。本文主要研究了DBN的基础模型RBM,并从特征学习的角度,利用RBM针对现有的应用在医学图像分类上的方法对医学图像的诊断做了两个方面的改进:1.提出利用RBM模型对医学图像进行特征学习的特征提取方法RBM是DBN模型的基础模型,它有一个二部图结构,是一个无向图,RBM本身也是一种有效的特征提取器。本文利用机器学习模型受限玻尔兹曼机的特征学习能力对图像的特征提取阶段进行改进,然后再利用组合分类器:基于Bagging的概率神经网络对图像进行分类。在乳腺X光图像的标准数据集(MIAS)上的实验结果表明:利用RBM学习到的特征进行的分类精度比起人工选择的分类精度更高。2.提出利用DRBM模型对医学图像分类的图像分类方法由于前面的改进存在一定程度的局限性,比如受限玻尔兹曼机学习到的特征可能并不适应于所有的分类器,所以本文又采用一种新的医学图像分析方法:判别式受限玻尔兹曼机(Discriminative Restricted Boltzmann Machine,DRBM)对医学图像进行分类分析。DRBM可以直接利用学习到的特征对图像进行分类,避免了特征形式与要求的数据形式不匹配的问题。DRBM是一种无向判别模型,它可以自动的从图像中学习特征,并利用学习到的特征直接对图像进行分类。在乳腺X光图像标准数据集上的实验结果表明,DRBM对医学图像的分类准确率要好于基于Bagging的概率神经网络利用学习到的特征对图像进行分类的效果。本文最后列出了目前医学图像分类研究中存在的一些问题以及今后需要进一步开展的研究工作。
[Abstract]:With the development of computer technology, more and more medical image analysis techniques have emerged. Data mining is one of the hotspots in medical image analysis. At present, many data mining methods have been successfully applied to medical image classification, but a large part of these methods are extracted first. At present, the main methods of analyzing medical images by feature extraction are association rules, decision trees, genetic algorithms, artificial neural networks, Bayesian networks, rough sets and support directions. However, the quality of feature extraction in medical image analysis method based on statistical feature extraction directly affects the result of image analysis, and the selection of features will be affected by subjective factors such as experience. In fact, DBN is a directed graph model. Its basic model is Restricted Boltzmann Machine (RBM). Because it is very difficult to infer the posterior distribution of hidden layer units in multi-layer directed graphs, the learning of DBN features is based on every one of them. This is because the RBM bipartite graph structure makes it easy to infer the state of hidden layer units. Each RBM learns that the state of its hidden layer units will be used as input data to the next layer of RBM, and by analogy, complete the training of DBN. Hinton et al. have demonstrated that each additional layer of RBM, DBN. DBN has been widely studied and applied because of its ability to learn complex high-level feature structures in input data. This paper mainly studies the basic model of DBN, RBM, and uses RBM from the point of view of feature learning. Two improvements have been made to the existing medical image classification methods: 1. RBM is the basic model of DBN model, which has a bipartite graph structure and is an undirected graph. RBM itself is an effective feature. 2. In this paper, the feature extraction stage of an image is improved by using the feature learning ability of a Boltzmann machine with machine learning model constraints, and then the image is classified by using a combined classifier: Bagging-based probabilistic neural network. The classification accuracy of the feature is higher than that of the manual selection. 2. The DRBM model is proposed to classify medical images because of the limitations of the previous improvements. For example, the features learned by the restricted Boltzmann machine may not be suitable for all classifiers, so this paper uses another one. A new method of medical image analysis: Discriminative Restricted Boltzmann Machine (DRBM) is used to classify and analyze medical images. DRBM can classify medical images directly by using the learned features, avoiding the problem that the feature form does not match the required data form. DRBM is an undirected discriminant. The experimental results on the mammogram standard dataset show that the classification accuracy of DRBM is better than that of Bagging-based probabilistic neural network. Finally, some problems in current medical image classification research and further research work in the future are listed.
【学位授予单位】：西北师范大学
【学位级别】：硕士
【学位授予年份】：2015
【分类号】：TP391.41
本文编号：2236378
[Abstract]:With the development of computer technology, more and more medical image analysis techniques have emerged. Data mining is one of the hotspots in medical image analysis. At present, many data mining methods have been successfully applied to medical image classification, but a large part of these methods are extracted first. At present, the main methods of analyzing medical images by feature extraction are association rules, decision trees, genetic algorithms, artificial neural networks, Bayesian networks, rough sets and support directions. However, the quality of feature extraction in medical image analysis method based on statistical feature extraction directly affects the result of image analysis, and the selection of features will be affected by subjective factors such as experience. In fact, DBN is a directed graph model. Its basic model is Restricted Boltzmann Machine (RBM). Because it is very difficult to infer the posterior distribution of hidden layer units in multi-layer directed graphs, the learning of DBN features is based on every one of them. This is because the RBM bipartite graph structure makes it easy to infer the state of hidden layer units. Each RBM learns that the state of its hidden layer units will be used as input data to the next layer of RBM, and by analogy, complete the training of DBN. Hinton et al. have demonstrated that each additional layer of RBM, DBN. DBN has been widely studied and applied because of its ability to learn complex high-level feature structures in input data. This paper mainly studies the basic model of DBN, RBM, and uses RBM from the point of view of feature learning. Two improvements have been made to the existing medical image classification methods: 1. RBM is the basic model of DBN model, which has a bipartite graph structure and is an undirected graph. RBM itself is an effective feature. 2. In this paper, the feature extraction stage of an image is improved by using the feature learning ability of a Boltzmann machine with machine learning model constraints, and then the image is classified by using a combined classifier: Bagging-based probabilistic neural network. The classification accuracy of the feature is higher than that of the manual selection. 2. The DRBM model is proposed to classify medical images because of the limitations of the previous improvements. For example, the features learned by the restricted Boltzmann machine may not be suitable for all classifiers, so this paper uses another one. A new method of medical image analysis: Discriminative Restricted Boltzmann Machine (DRBM) is used to classify and analyze medical images. DRBM can classify medical images directly by using the learned features, avoiding the problem that the feature form does not match the required data form. DRBM is an undirected discriminant. The experimental results on the mammogram standard dataset show that the classification accuracy of DRBM is better than that of Bagging-based probabilistic neural network. Finally, some problems in current medical image classification research and further research work in the future are listed.
【学位授予单位】：西北师范大学
【学位级别】：硕士
【学位授予年份】：2015
【分类号】：TP391.41
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