基于小波分析理论的医学图像配准研究
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摘要
随着信息技术的发展,各类医学成像设备陆续应用于临床医学,医学图像随之成为疾病诊治的重要辅助工具,有效地提高了临床医学诊治的技术水平。医学图像处理的研究已成为前沿研究领域,而医学图像配准是其中非常重要的研究热点之一,医学图像增强也是医学图像后续处理及应用的基础。本文围绕医学图像配准和增强这两类问题进行了研究,并针对配准精度、配准数据量的问题及增强质量的要求,着重研究了基于多尺度分析的医学图像配准和增强策略。本文的主要工作包括：
(1)针对医学图像增强,提出两种基于多小波变换的增强方法。方法一是在小波变换的基础上,采用Haar变换对高频子带系数继续进行分解,得到多小波分解的高频子带,然后对滤噪后不同子带的高频系数使用不同的增强因子进行增强处理,当图像重构后,再利用分段灰度变换获取灰度范围更丰富的增强图像。方法二是多级多层增强方法,即在多小波分解后,先对多小波高频系数进行增强,然后对经Haar逆变换重构的高频子带继续进行增强处理,此外,把每一级的低频子带也使用非线性算子进行增强处理,以获取更好的增强效果。图像重构后,再对图像进行分段灰度变换,以获取灰度范围更丰富的增强图像。实验结果证明这两种增强方法是有效可行的。
(2)针对医学图像刚性配准,提出两种基于多尺度分析的粗、细配准相融合的配准方法。在方法一的精细配准过程中,首先提出一种简化的多小波变换,进而提出了基于简化多小波变换的医学图像配准方法,即对由小波分解得到的部分高频子带做进一步分解,并提取这些高频子带的近似信息作为配准对象,既有效的减少了运算量,又充分利用了原图像的高频信息,从而保证了配准结果的准确性。在方法二的逐层配准过程中,首先利用采样模板得到各级重采样图像,然后从最后一级重采样图像开始配准,直到原始图像结束。这两种方法的粗配准都是针对图像的轮廓特征采用主轴法来实现。实验结果证明这两种配准方法都能取得理想的配准结果。
(3)针对医学图像非刚性配准,提出一种基于薄板样条逐步逼近的非刚性配准方法。首先利用扫描线法提取两幅图像中变形区域的边界,接着选择局部配准区域,使得配准过程只在局部进行,避免全局的变形问题。然后利用从形变区域中心引出射线的方法自动提取控制点,再使用薄板样条获得第一次配准结果,接着进行逐步逼近配准,即每次配准都在前一次配准的结果基础上使用薄板样条继续进行配准,直到满足给定的约束条件结束。实验结果证明该配准方法能够准确地实现医学图像的非刚性配准。
Along with the development of information technology, all kinds of medical imaging e-quipments were applied into the clinical medicine field. Medical images have been become the important auxiliary tools for diagnosis and treatments of diseases. And the technology of clinical diagnosis and treatments has been improved effectively. Studies on the medical image processing have been become the frontier research field and one of hot studies. In all of studies on the medical image processing, the registration of medical images was one of very impor-tant research directions. And the enhancement of the medical image was also the foundation of medical images processing and their applications. In this dissertation, we concerned with the registration and enhancement processing of medical images, and have done some researches with multi-scale analysis on the precision, the quantity of data of registration and the quality of enhancement, and so on. The main work could be summarized as follows:
(1) We presented two methods for the enhancement of medical images based on multi-wavelet transform. In the first algorithm, the medical image to be enhanced was decomposed with wavelet transform, and all high-frequency coefficients were decomposed by Haar transform again. Then all high-frequency coefficients were enhanced by different enhancement weight value after they were de-noised by soft-threshold method. In the following, the image's gray was transformed by the piecewise linear transformation after it was reconstructed. Thus the enhanced image was obtained. In the second algorithm, both high-frequency coefficients of multiwavelet transform and high-frequency coefficients of single wavelet transform were all enhanced by different enhancement weight values. In addition, all of low-frequency coefficients in every decomposition level were enhanced by a non-linear operator. Experiments have showed that the proposed methods can not only enhance an image's details but also hold its edge features effectively.
(2) We presented two methods for the rigid registration of medical images based on multi-scale analysis, which all consisted of two main procedures, i.e. the rough registration and the fine registration. In the fine registration procedure of the first algorithm, the couple medical images were decomposed by the simplified multiwavelet transform. And only the high fre-quency coefficients in the horizontal directions were selected as registration objects. Then the registration process was started from the coarse scale, and ended to the fine scale based on the images'high frequency coefficients and the rough registration information. At last, the couple initial images were selected as registration objects to accomplish the last registration based on all the above registration information. In the fine registration procedure of the second algorith-m, down-sample images were obtained by a sampling operator level by level. Then they were registered from the last level down-sample image to the initial image. In the rough registration procedure of the couple algorithms, contour features of images to be registered were extracted at first, then the principal axes method was used to obtain the translation and rotation infor-mation based on images'contour features. Experiments have showed that they were effective and accurate registration methods of medical images. Furthermore, the results demonstrated the accurate registration under noisy environment too.
(3) We presented a successive approximation registration method based on the thin-plate s-plines for non-rigid medical images registration. The regions of interesting in the couple images were extracted at first, and the control points of the interesting region were selected automati-cally. Secondly, the successive approximation registration method was used to accomplish the non-rigid medical images registration, i.e., the local regions of the couple images were registered roughly based on the thin-plate splines, then, the current rough registration results were selected as the objects to be registered in the following registration procedure. Experiments showed that the proposed method was effective in the registration process of the non-rigid medical images.
(1)针对医学图像增强,提出两种基于多小波变换的增强方法。方法一是在小波变换的基础上,采用Haar变换对高频子带系数继续进行分解,得到多小波分解的高频子带,然后对滤噪后不同子带的高频系数使用不同的增强因子进行增强处理,当图像重构后,再利用分段灰度变换获取灰度范围更丰富的增强图像。方法二是多级多层增强方法,即在多小波分解后,先对多小波高频系数进行增强,然后对经Haar逆变换重构的高频子带继续进行增强处理,此外,把每一级的低频子带也使用非线性算子进行增强处理,以获取更好的增强效果。图像重构后,再对图像进行分段灰度变换,以获取灰度范围更丰富的增强图像。实验结果证明这两种增强方法是有效可行的。
(2)针对医学图像刚性配准,提出两种基于多尺度分析的粗、细配准相融合的配准方法。在方法一的精细配准过程中,首先提出一种简化的多小波变换,进而提出了基于简化多小波变换的医学图像配准方法,即对由小波分解得到的部分高频子带做进一步分解,并提取这些高频子带的近似信息作为配准对象,既有效的减少了运算量,又充分利用了原图像的高频信息,从而保证了配准结果的准确性。在方法二的逐层配准过程中,首先利用采样模板得到各级重采样图像,然后从最后一级重采样图像开始配准,直到原始图像结束。这两种方法的粗配准都是针对图像的轮廓特征采用主轴法来实现。实验结果证明这两种配准方法都能取得理想的配准结果。
(3)针对医学图像非刚性配准,提出一种基于薄板样条逐步逼近的非刚性配准方法。首先利用扫描线法提取两幅图像中变形区域的边界,接着选择局部配准区域,使得配准过程只在局部进行,避免全局的变形问题。然后利用从形变区域中心引出射线的方法自动提取控制点,再使用薄板样条获得第一次配准结果,接着进行逐步逼近配准,即每次配准都在前一次配准的结果基础上使用薄板样条继续进行配准,直到满足给定的约束条件结束。实验结果证明该配准方法能够准确地实现医学图像的非刚性配准。
Along with the development of information technology, all kinds of medical imaging e-quipments were applied into the clinical medicine field. Medical images have been become the important auxiliary tools for diagnosis and treatments of diseases. And the technology of clinical diagnosis and treatments has been improved effectively. Studies on the medical image processing have been become the frontier research field and one of hot studies. In all of studies on the medical image processing, the registration of medical images was one of very impor-tant research directions. And the enhancement of the medical image was also the foundation of medical images processing and their applications. In this dissertation, we concerned with the registration and enhancement processing of medical images, and have done some researches with multi-scale analysis on the precision, the quantity of data of registration and the quality of enhancement, and so on. The main work could be summarized as follows:
(1) We presented two methods for the enhancement of medical images based on multi-wavelet transform. In the first algorithm, the medical image to be enhanced was decomposed with wavelet transform, and all high-frequency coefficients were decomposed by Haar transform again. Then all high-frequency coefficients were enhanced by different enhancement weight value after they were de-noised by soft-threshold method. In the following, the image's gray was transformed by the piecewise linear transformation after it was reconstructed. Thus the enhanced image was obtained. In the second algorithm, both high-frequency coefficients of multiwavelet transform and high-frequency coefficients of single wavelet transform were all enhanced by different enhancement weight values. In addition, all of low-frequency coefficients in every decomposition level were enhanced by a non-linear operator. Experiments have showed that the proposed methods can not only enhance an image's details but also hold its edge features effectively.
(2) We presented two methods for the rigid registration of medical images based on multi-scale analysis, which all consisted of two main procedures, i.e. the rough registration and the fine registration. In the fine registration procedure of the first algorithm, the couple medical images were decomposed by the simplified multiwavelet transform. And only the high fre-quency coefficients in the horizontal directions were selected as registration objects. Then the registration process was started from the coarse scale, and ended to the fine scale based on the images'high frequency coefficients and the rough registration information. At last, the couple initial images were selected as registration objects to accomplish the last registration based on all the above registration information. In the fine registration procedure of the second algorith-m, down-sample images were obtained by a sampling operator level by level. Then they were registered from the last level down-sample image to the initial image. In the rough registration procedure of the couple algorithms, contour features of images to be registered were extracted at first, then the principal axes method was used to obtain the translation and rotation infor-mation based on images'contour features. Experiments have showed that they were effective and accurate registration methods of medical images. Furthermore, the results demonstrated the accurate registration under noisy environment too.
(3) We presented a successive approximation registration method based on the thin-plate s-plines for non-rigid medical images registration. The regions of interesting in the couple images were extracted at first, and the control points of the interesting region were selected automati-cally. Secondly, the successive approximation registration method was used to accomplish the non-rigid medical images registration, i.e., the local regions of the couple images were registered roughly based on the thin-plate splines, then, the current rough registration results were selected as the objects to be registered in the following registration procedure. Experiments showed that the proposed method was effective in the registration process of the non-rigid medical images.
引文
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