【作者】 柯珊虹；

【导师】 林土胜；

【作者基本信息】 华南理工大学 ， 信号与信息处理， 2010， 硕士

【摘要】 医学图像在临床医学上应用越来越广泛,使得图像分割这一医学图像处理和分析中的基本问题也越来越关键。医学图像分割是指将医学图像分割为一系列相互不交叠的区域,这些区域具有相似的特征,例如灰度、色彩、纹理、局部统计特征或频谱等。分割后的图像可以被广泛应用到多种场合,如组织容积的定量分析、计算机辅助诊断、计算机引导手术、病变组织的定位、局部容积效应的矫正等等。由于解剖结构复杂多样以及成像设备、成像技术的制约,医学图像不可避免地受到各种退化因素的影响,包括噪声、局部容积效应和有偏场效应等。目前,尽管医学图像分割算法种类繁多,但是仍然没有一种完美的自动分割算法,各种算法都只能针对某些特定的条件或场合取得较佳的分割效果。本文对二维MRI(Magnetic Resonance Imaging)图像分割中核聚类算法的应用进行了一定研究,对传统FCM(Fuzzy C-means Clustering)算法和KFCM-II(Kernel-based FCM II)算法应用于MRI图像分割加入了灰度有偏场的纠正,进行实验获得实验结果并进行性能分析。实验结果表明,KFCM-II算法对低退化条件的MRI图像的分割任务,结果并不能比FCM算法占优;对于高退化条件的分割任务,则应采用结合灰度有偏场的纠正的算法;若追求算法对不同退化条件下分割的稳定性与准确度的综合平衡,则应该选择结合灰度纠正的KFCM-II算法。文章首先介绍了医学图像分割的相关背景、MRI成像机理和分割目标,以及分割结果的评估方法。接着阐述了模糊理论与模糊聚类的相关内容,详细介绍了经典的FCM算法,分析了它的性能及缺点。然后引入核方法思想,阐明核方法的优势,重点阐述KFCM-I和KFCM-II算法。继而提出结合灰度有偏场的纠正以改善分割算法性能,介绍了KFCM-III算法原理以及与之相关的聚类典型集和数据分类等概念。最后使用VC++调用Matlab计算引擎对分割算法进行了软件设计并实验。更多还原

【Abstract】 The application of medical images in clinical medicine is becoming more and more widespread, that makes the image segmentation which is the fundamental problem of medical image processing and analysis in the increasingly critical. Medical image segmentation refers to the process of partitioning observed image data to a serial of non-overlapping regions, which have similar features, such as grayscale, color, texture, local statistical features and spectrum, etc. Segmented images can be widely applied in various applications, such as tissue volume quantitative analysis, computer-aided diagnosis, computer guided surgery, lesion tissue location and partial volume effect correction, etc.Due to the complexity and varieties of anatomy structures as well as the imperfections of imaging scanner and imaging techniques, obtained medical images will inevitably be affected by lots of corruption factors including additive noises, partial volume effect and intensity bias field. There is still not a perfect auto segmentation algorithm so far, despite the fact that there exist extensive medical image segmenting methods. Various algorithms are only for certain specific conditions or situations to obtain better segmentation.This dissertation has studied the application of kernel based clustering algorithms for segmenting two-dimensional MRI (Magnetic Resonance Imaging) medical images, adopted correction method for intensity bias field of MRI data to the traditional FCM (Fuzzy C-means Clustering) algorithm and KFCM-II (Kernel-based FCM II) algorithm, and carried out experiments to obtain segmentation results for performance analysis. The experiment results show that, KFCM-II algorithm is not better than FCM algorithm when applied to segment MRI images with low level degraded conditions; As to segmentation tasks with high level degraded conditions, algorithms with correction of intensity bias field should be adopted; If overall balance of accuracy and stability for the segmentation results under different level degrade conditions is the goal, KFCM-II algorithm with correction of intensity bias field should be chosen.The dissertation first introduces the background of medical image segmentation, MRI imaging mechanism, the segmentation target, and the assessment rules for segmentation results. Then, expounds the theory of fuzzy set and fuzzy clustering, goes into details for the classical FCM algorithm, with a analysis for its performance and shortcomings. The thought of kernel methods is set forth with its advantages clarified, focusing on the KFCM-I and KFCM-II algorithm. And then adopting the correction method of intensity bias field to improve the segmentation algorithm performance is proposed. The KFCM-III and its related concept of typical clustering dataset and data classification are introduced. Finally, using the programming skill of VC + + calling Matlab calculation engine to implement the algorithms and carried out segmentation experiments.更多还原