基于张量分析的脑部医学图像识别
发布时间：2018-10-11 19:10
【摘要】：随着计算机领域的不断创新和发展,医学成像技术也在不断地提高。医学图像识别作为诊疗病情的关键技术手段,在医学研究和临床试验方面需求庞大,发展迅速。作为是人体内最复杂也是最重要的器官,关于大脑的相关医学研究非常依赖医学图像识别。脑部医学图像识别的基础包括成像技术,脑部结构特征提取和分类等,因此脑部医学图像识别作为多学科交叉的领域,具有非常高的研究价值和意义。由于脑部医学图像的数据相对于一般图像数据而言,其数据本质是三维空间结构的体素数据,传统上对图像特征提取和分类算法的研究习惯于从向量的角度出发来考虑问题,然而这样却忽略了图像结构上的特点,从而破坏了图像的高阶信息,这种图像高阶结构信息的损失不仅导致了图像识别率的损失,还造成了居高不下的计算复杂度。如何能够在保存空间结构信息的同时对高维空间结构数据进行特征提取和分类,成为现如今医学图像识别领域的一个问题。本文结合高维空间结构数据,以数据张量化为研究重点,帮助脑部医学图像数据进行整体的特征提取提出改进,结合基于循环卷积的张量模型以及张量主成分分析(简称TPCA)的理论知识以及相关的基本概念,能够广泛的对各种医学图像数据进行张量数据分析。同时,本文以时下流行的SBD数据集,结合基于张量模型的主成分分析对数据张量化后的数据集进行提取图像特征,用所提取的图像特征对SBD医学图像进行识别分类,验证并分析最后的处理结果。实验证明,数据张量化方法在提取脑部医学图像这样的高维空间结构数据的图像特征方面具有良好的适用性,数据张量化后基于张量模型的算法比基于向量的算法要好。
[Abstract]:With the innovation and development of computer field, medical imaging technology is improving. Medical image recognition, as a key technique in diagnosis and treatment, has a huge demand for medical research and clinical trials. As one of the most complex and important organs in the human body, medical research on the brain relies heavily on medical image recognition. The basis of brain medical image recognition includes imaging technology, brain structure feature extraction and classification. Therefore, as a multidisciplinary field, brain medical image recognition is of great value and significance. Because the data of brain medical image is essentially voxel data with three-dimensional structure compared with general image data, the traditional research on image feature extraction and classification algorithm is used to consider the problem from the point of view of vector. However, the characteristics of the image structure are ignored and the higher-order information of the image is destroyed. The loss of the higher-order structure information of the image not only leads to the loss of the recognition rate of the image, but also results in a high computational complexity. How to extract and classify high-dimensional spatial structure data while preserving spatial structure information has become a problem in the field of medical image recognition nowadays. Based on high-dimensional spatial structure data and data tensor quantization, this paper proposes an improved method to help the whole feature extraction of brain medical image data. Combined with Zhang Liang model based on cyclic convolution and the theoretical knowledge and related basic concepts of Zhang Liang principal component analysis (TPCA), it can be widely used to analyze all kinds of medical image data. At the same time, using the popular SBD data set and principal component analysis (PCA) based on Zhang Liang model, this paper extracts the image feature of the tensor data set, and classifies the SBD medical image with the extracted image feature. Verify and analyze the final processing results. Experiments show that data tensor quantization method has good applicability in extracting image features of high-dimensional spatial structure data such as brain medical image. The algorithm based on Zhang Liang model after data tensor is better than that based on vector.
【学位授予单位】：中原工学院
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TP391.41
本文编号：2264940
[Abstract]:With the innovation and development of computer field, medical imaging technology is improving. Medical image recognition, as a key technique in diagnosis and treatment, has a huge demand for medical research and clinical trials. As one of the most complex and important organs in the human body, medical research on the brain relies heavily on medical image recognition. The basis of brain medical image recognition includes imaging technology, brain structure feature extraction and classification. Therefore, as a multidisciplinary field, brain medical image recognition is of great value and significance. Because the data of brain medical image is essentially voxel data with three-dimensional structure compared with general image data, the traditional research on image feature extraction and classification algorithm is used to consider the problem from the point of view of vector. However, the characteristics of the image structure are ignored and the higher-order information of the image is destroyed. The loss of the higher-order structure information of the image not only leads to the loss of the recognition rate of the image, but also results in a high computational complexity. How to extract and classify high-dimensional spatial structure data while preserving spatial structure information has become a problem in the field of medical image recognition nowadays. Based on high-dimensional spatial structure data and data tensor quantization, this paper proposes an improved method to help the whole feature extraction of brain medical image data. Combined with Zhang Liang model based on cyclic convolution and the theoretical knowledge and related basic concepts of Zhang Liang principal component analysis (TPCA), it can be widely used to analyze all kinds of medical image data. At the same time, using the popular SBD data set and principal component analysis (PCA) based on Zhang Liang model, this paper extracts the image feature of the tensor data set, and classifies the SBD medical image with the extracted image feature. Verify and analyze the final processing results. Experiments show that data tensor quantization method has good applicability in extracting image features of high-dimensional spatial structure data such as brain medical image. The algorithm based on Zhang Liang model after data tensor is better than that based on vector.
【学位授予单位】：中原工学院
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TP391.41
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