摘要
医学图像分割是医学图像进一步处理和分析的前提条件和基础,在医学图像处理中是一个经典的问题,在临床诊断、病理分析以及治疗等方面具有非常重要的意义。对于脑组织图像而言,对脑部组织的正确分割在测量病变组织的尺寸、外观的量化信息和实现脑部结构的三维重构等问题中起到了关键作用。核磁共振脑图像本身具有模糊性,而且成像过程中容易受到噪声、局部体效应和亮度不均匀性等因素的影响,使图像中某一个特定组织的亮度分布变得平滑,从而导致相邻组织间的亮度直方图交叠分布,成为脑组织图像自动分割技术研究中一个主要障碍,对分割结果的准确性和可靠性造成了很大影响。
为了提高核磁共振脑图像分割算法的准确性,针对脑图像分割中的噪声问题和偏移场问题,本文根据核磁共振脑图像的特点,建立了基于聚类区域的图像模型,综合利用模糊C均值算法、信息熵理论、基于梯度的分水岭算法、基于小波的去噪算法和脑组织图像分布规律等,分别提出一种基于局部熵最小化的核磁共振脑图像二次分割算法、基于区域动态搜索和基于二次去噪的核磁共振脑图像分割算法。本文使用的模拟数据和真实数据来自蒙特利尔神经学院的脑组织图像中心和国际脑组织图库,并通过Matlab语言编程实现。通过大量的实验分析和比较,证明了本文算法的准确性和可靠性。
The medical image segmentation technique is the key technology in medical image processing and analysis, which is one of the classical problems in medical image processing. The medical image segmentation is the precondition and basis for the understanding and explication of the medical image, and be of great importance in the clinical diagnosis, the pathological analysis and the treatment. It is commonly used in image analysis, registration, fusion, the measurement of anatomical structure and image reconstruction. Specifically, the results of image segmentation can be used to measure the volume of human organs, tissues or lesions. According to the quantitative measurement and analysis of the volume before and after the treatment, the doctor can predict, diagnose and develop the patient's treatment plan; the results can be used on the reconstruction of3D data, visualization, the formulation and simulation of the surgical program; the results of the image segmentation can also be used for the remote medical expert system. In the case of network bandwidth resources are limited, the accurate image segmentation is critical for improving the local image classification based on the region of interest(ROI) and the progressive transmission speed. In order to obtain the quantitative information of the size and the appearance of brain lesions, and realize the three-dimensional reconstruction of brain structure, the segmentation of brain image is particularly critical.During medical image segmentation, noise, partial volume effects and intensity inhomogeneity are three mainly considerable difficulties. The existence of noise makes the segmentation region becomes discontinuous, directly affects the accuracy of segmentation results. The intensity inhomogeneity (also known as non-uniformity field), is a phenomenon of the brightness change slowly, in the same physiological organization or structure. It includes both shading artifacts and inherent non-uniform of tissue properties. And in the whole image, the bias field is continuous, smooth and slowly varying. It is mainly caused by the imperfection of the magnetic resonance imaging equipment, including the RF field, the static field and gradient field. The inherent non-uniform of tissue properties is that, there exist quite a few spatially different substructures with functions within each tissue class in the human brain. Due to the inherent regional differences in imaging-related properties across substructures, the intensities in different substructures, even in the same tissue class, are also more or less different. The imaging-related properties that cause the inherent intensity variation include the composition, density, and magnetic properties (spin-lattice relaxation time T1, spin-spin relaxation time T2) of different tissues at different positions.
Although the affect of intensity inhomogeneities on MR images is not obvious on visually, because the human visual system can automatically correct the inhomogeneity. However, intensity inhomogeneities in MR images, which can change the local statistical characteristics of the image, and cause the brightness distribution of different physiological organizations overlap, and the segmentation of MR images is more difficulty than other image, are a major obstacle to any automatic methods for MR image.
In order to improve the accuracy of the segmentation algorithm for Magnetic resonance brain image, we research the problem of the noise and intensity inhomogeneity in the brain image segmentation, in-depth research on the segmentation algorithm for Magnetic resonance brain images corrupted by intensity inhomogeneity. The main work of this paper is as follows:
(1) Based on local image model, we propose a Secondary segmentation algorithm for Magnetic Resonance Brain image based on Local Entropy Minimization(SLEM), to overcome the impact of the intensity inhomogeneity. The tissue-based block method meets the local image model, which makes it is possible to overcome the impact of intensity inhomogeneity by using the idea of segmentation based on local region. Secondly, the use of information entropy theory realizes the optimization of segmentation region, makes the algorithm to keep the localized and find the minimum area of the region affected by the intensity inhomogeneity at the same time, so improve the accuracy and computing time of the algorithm. Then, for each segmentation region, the use of FCM algorithm is not only suitable for brain tissue images with fuzzy characteristics, but also run fast. Finally, the regional dynamic search of secondary segmentation algorithm, achieves the secondary segmentation for the misclassification pixels in the first segmentation result, further improve the accuracy of the algorithm.
(2) We use the over-segmentation of watershed algorithm as an advantage to overcome the intensity inhomogeneity in the image, and proposed a segmentation algorithm based on Regional Dynamic Search (RDS) for MR brain images corrupted by intensity inhomogeneity. Regional dynamic search is established on the basis of two concepts of search window and segmentation region(also segmentation environment), which achieves the dynamic corresponding relationship between the window and region, not only improves the ability of the algorithm to overcome the intensity inhomogeneity, but also solves the problem of boundary effects appear in the existing segmentation algorithm based on region. We use the characteristics of over-segmentation region and tag matrix obtained by the watershed algorithm, to find the region segmentation environment for search window which satisfy the conditions continuously. Finally, the FCM algorithm is independently performed in the final segmentation regions corresponding to each search window, to determine the segmentation results of the pixels in the search window, thus completing the fast and accurate segmentation of the whole image.
(3) We use the "denoising" operation on the image of segmentation result, and propose a secondary denoising algorithm for the segmentation of magnetic resonance brain tissue image with noise and intensity inhomogeneity. Firstly, in order to reduce the impact of noise, we use a half-soft threshold denoising algorithm based on wavelet to remove the noise in image. Secondly, the segmentation algorithm based on regional dynamic search for MR brain images proposed in the previous chapter, is used to reduce the impact of the intensity inhomogeneity for segmenting the image after denoising. Due to the influence of noise, there are significant misclassification points in the segmentation result, we call "noise points". Therefore, we proposed the secondary denoising algorithm for the segmented image, to segment the pixels which meet the conditions of noise point again. The experimental results show that when the noise level is high in the image, the secondary denoising algorithm improves the accuracy of the segmentation results significantly better than the denoising algorithm before segmentation.
Above all, this thesis makes an in-depth study on the segmentation algorithm of Magnetic Resonance brain image, the main consideration is the impact of the noise and bias field on the segmentation results. The proposed methods overcome the impact of the noise and bias field, and realize the segmentation of MR brain images rapidly and accurately at the same time. The experiment is implemented on both simulated and real MR brain images, and compared with other published algorithm, prove the validity and accuracy of our proposed methods in terms of the accuracy and run time of the segmentation results.
引文
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