MRI图像的脑肿瘤分割方法研究
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摘要
核磁共振成像(MRI)是医学影像中的一种重要成像技术,由于其高质量的图像显示效果,现在已经被广泛的应用到人体各种组织器官病变的诊断,其中较为主要的一个应用就是检测脑部病变组织。MRI脑肿瘤分割为临床诊断和治疗提供了很好的基础,但如何分割MRI脑肿瘤不仅是临床诊断与治疗,也是MRI脑肿瘤图像研究的一个重点和难点。本文将改进现有MRI脑肿瘤图像分割算法,利用模糊C均值算法、区域增长算法、以及结合模糊相似度理论和区域结构识别技术的分割算法实现脑胶质瘤MRI图像的分割。
首先,简略的介绍现有的MRI脑肿瘤图像分割算法的原理,以及这些算法应用在MRI脑肿瘤图像分割中的优缺点。介绍MRI成像原理、MRI成像的优点和缺点,为后续研究奠定基础理论知识。
其次,针对极具代表性的模糊c均值算法,确定了FCM算法在脑MRI图像分割的应用中相关参数的选择问题,指出参数的优化选择对算法的性能和速度的影响,实现了较为精准的肿瘤分割。
再次,针对区域生长对噪声敏感以及生长阈值选取困难的不足,本文提出了基于边缘平均梯度和类内平均方差的模型。在图像分割的预处理过程中,首先进行各向异性滤波,达到平滑滤波去除噪声影响的同时避免了边界的模糊,接着引入基于边沿平均梯度和类内平均方差的模型,以边沿平均梯度的倒数和类内平均方差的和作为目标函数,取目标函数的极小值为约束条件。在区域生长的过程中,逐渐增加生长阈值门限,通过目标函数来优化图像分割。我们选取得到目标函数极小值的生长门限值作为最终的分割结果。在实际的脑肿瘤MRI图像分割结果表明,该模型合理有效。
最后,基于模糊相似度理论的MRI分割算法依赖于初始条件,而图像可以看作是一个网络结构,区域结构对于分析网络的系统结构很有利,同时基于结点相似性的区域结构检测技术不依赖于结点的初始假设,因此,提出基于模糊相似度理论和区域结构检测技术的MRI分割算法,该算法具有快速、鲁棒等特点。
Magnetic Resonance Imaging (MRI) is an important medical diagnostic tool, because of its high quality in image display, which now has been widely applied to detect pathological lesions and diseases in various tissues and organs, especially in tumor detection. The segmentation of tumor is important and of great significance in clinical and scientific research. Although many segmentation algorithms have been proposed, most have limitations. The dissertation focuses on the Fuzzy C Means (FCM) algorithm, Region Growing method, and a segmentation approach of brain tumor which combines the fuzzy affinity technology with the community structure detection technique based on the node similarity is proposed, and tries to improve the performance of them.
Firstly, the dissertation briefly describes the characteristics and principles of many algorithms in medical image segmentation, also their advantages and disadvantages in MRI tumor segmentation. As the same time, the dissertation briefly describes the principle of MR imaging.
Secondly, as the representative algorithm, FCM method is discussed emphatically in the dissertation. We analyze the parameter selection in FCM, and achieve the accurate tumor segmentation.
Thirdly, to overcome the difficulty of manual threshold selection and sensitivity to noise in region growing method, an adaptive region growing method based on the gradients and variances along and inside of the boundary curve is proposed. Firstly, we use the anisotropic diffusion filter to preserve the edge information. Then the new model is given, which chooses the mean variance inside of the boundary curve and the reciprocal of the mean gradient along the curve as the research subject. The objective function of the model is to add two elements about gradient and variance mentioned. The minimum of the sum is the optimum result which corresponding to the desirable threshold. In region growing processing step, the threshold is increased gradually and the set of the coarse contour is obtained. Finally, through optimizing the model, the optimal segmentation result can be acquired from the set of contours. In clinical MRI image segmentation, our method can produce very satisfactory results.
In the last, the method based on the fuzzy affinity depends on the initial conditions. An image is viewed as a network, and the detection of the community structure in networks is beneficial to understand the network structure and to analyze the network properties. The community structure is discovered based on node similarity, which is fast and efficient. Then a new segmentation approach of brain tumor which combines the fuzzy affinity technology with the community structure detection technique based on the node similarity is proposed in the dissertation.
首先,简略的介绍现有的MRI脑肿瘤图像分割算法的原理,以及这些算法应用在MRI脑肿瘤图像分割中的优缺点。介绍MRI成像原理、MRI成像的优点和缺点,为后续研究奠定基础理论知识。
其次,针对极具代表性的模糊c均值算法,确定了FCM算法在脑MRI图像分割的应用中相关参数的选择问题,指出参数的优化选择对算法的性能和速度的影响,实现了较为精准的肿瘤分割。
再次,针对区域生长对噪声敏感以及生长阈值选取困难的不足,本文提出了基于边缘平均梯度和类内平均方差的模型。在图像分割的预处理过程中,首先进行各向异性滤波,达到平滑滤波去除噪声影响的同时避免了边界的模糊,接着引入基于边沿平均梯度和类内平均方差的模型,以边沿平均梯度的倒数和类内平均方差的和作为目标函数,取目标函数的极小值为约束条件。在区域生长的过程中,逐渐增加生长阈值门限,通过目标函数来优化图像分割。我们选取得到目标函数极小值的生长门限值作为最终的分割结果。在实际的脑肿瘤MRI图像分割结果表明,该模型合理有效。
最后,基于模糊相似度理论的MRI分割算法依赖于初始条件,而图像可以看作是一个网络结构,区域结构对于分析网络的系统结构很有利,同时基于结点相似性的区域结构检测技术不依赖于结点的初始假设,因此,提出基于模糊相似度理论和区域结构检测技术的MRI分割算法,该算法具有快速、鲁棒等特点。
Magnetic Resonance Imaging (MRI) is an important medical diagnostic tool, because of its high quality in image display, which now has been widely applied to detect pathological lesions and diseases in various tissues and organs, especially in tumor detection. The segmentation of tumor is important and of great significance in clinical and scientific research. Although many segmentation algorithms have been proposed, most have limitations. The dissertation focuses on the Fuzzy C Means (FCM) algorithm, Region Growing method, and a segmentation approach of brain tumor which combines the fuzzy affinity technology with the community structure detection technique based on the node similarity is proposed, and tries to improve the performance of them.
Firstly, the dissertation briefly describes the characteristics and principles of many algorithms in medical image segmentation, also their advantages and disadvantages in MRI tumor segmentation. As the same time, the dissertation briefly describes the principle of MR imaging.
Secondly, as the representative algorithm, FCM method is discussed emphatically in the dissertation. We analyze the parameter selection in FCM, and achieve the accurate tumor segmentation.
Thirdly, to overcome the difficulty of manual threshold selection and sensitivity to noise in region growing method, an adaptive region growing method based on the gradients and variances along and inside of the boundary curve is proposed. Firstly, we use the anisotropic diffusion filter to preserve the edge information. Then the new model is given, which chooses the mean variance inside of the boundary curve and the reciprocal of the mean gradient along the curve as the research subject. The objective function of the model is to add two elements about gradient and variance mentioned. The minimum of the sum is the optimum result which corresponding to the desirable threshold. In region growing processing step, the threshold is increased gradually and the set of the coarse contour is obtained. Finally, through optimizing the model, the optimal segmentation result can be acquired from the set of contours. In clinical MRI image segmentation, our method can produce very satisfactory results.
In the last, the method based on the fuzzy affinity depends on the initial conditions. An image is viewed as a network, and the detection of the community structure in networks is beneficial to understand the network structure and to analyze the network properties. The community structure is discovered based on node similarity, which is fast and efficient. Then a new segmentation approach of brain tumor which combines the fuzzy affinity technology with the community structure detection technique based on the node similarity is proposed in the dissertation.
引文
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