Research On Multiresolution Medical Image Registration And Adaptive Image Interpolation Technology

Image registration plays a crucial role in medical image analysis by providing comparative and/or complementary information from multimodal images or images taken at different times for the same or different subjects. Automated image registration has thus been widely used in clinical applications, such as diagnosis, staging, assessment of the response to the treatment, and image guided surgery.With the advance of medical imaging techniques, the image data size increases dramatically. As a result, the computational complexity for image registration, in particular three-dimensional (3D) image registration, increases exponentially, which leads to a higher possibility of mismatching, i.e., the current intensity-based similarity measures may likely be trapped into local extrema. While multiresolution analysis (MRA), such as wavelet transforms, provides a potential mechanism to improve the registration accuracy and reduce the computational complexity, it does not have the necessary properties, such as translation- and rotation- invariance, required for image registration.In many image processing applications, it is necessary to interpolate digital images so that high resolution images can be obtained from low resolution images. For example, digital TV signals are transformed into signals that match those of high-definition television (HDTV) receivers. Image interpolation algorithms are also in demand in the field of remote sensing and sensor networking, where low resolution images are usually captured by inexpensive imaging devices.In practice, simple interpolation methods, such as nearest-neighbor, linear, bilinear, and bicubic interpolation are most widely used. However, these methods usually yield an interpolated image with blurred edges, which degrade the perceptual quality of the image. Improving the subjective quality and reducing the computational complexity of interpolation algorithms are important issues in video and network signal processing.In multiresolution medical image registration and image interpolation, the main contributions in this thesis including:1. The translation- and rotation- invariance of the multi-resolution analysis improvesthe registration accuracy and avoids trapping in local extrema which frequentlyleads to misregistration. A new multiresolution analysis, improved circular symmetric multiresolution decomposition is proposed. An annular band-pass filter takes place of the high pass filter in the circular symmetric multiresolution analysis to reduce the redundancy. All the subbands possess translation- and rotation-invariance.2. The improved circular symmetric image pyramid decomposition with translation-and rotation- invariant properties can improve the performance of image registration. Low-pass subband has noise-removing property and is suitable to image registration based on mutual information. Band-pass subband has more significant structural information, which establishes image fuzzy gradient field and constructs the fuzzy approach degree. A coarse-to-fine procedure is adopted to utilize these features to achieve registration procedure. Experiments demonstrate the good performance of the proposed novel pyramid decomposition. The local extrema can be reduced and these characteristics of combined measures yield more robust and accurate registration results.3. To improve the medical image registration accuracy and efficiency, a new approach of image registration based on circular symmetric multiresolution decomposition and edge information is proposed. Firstly low pass subbands in the multiresolution decomposition preserve the global image information, and are utilized to perform image registration based on mutual information. Then the cross-weighted moments are calculated in the first level of band pass subband which includes sufficient spatial information. It provides the initial transformation parameters to the hierarchical registration. So the transformation displacements will be calculated rapidly and accurately in the optimization algorithm. Experiments demonstrate that the intensity and edge information combined method based on circular symmetric pyramid improves the registration accuracy and robustness. It also reduces the iterations of optimization and has low computation complexity.4. A novel 3D spherical symmetric multiresolution pyramid (SSMP) is proposed. In our SSMP, McClellan algorithm is applied to build the sphere and spherical ring 3D filter banks. SSMP can be used to generate a multiresolution representation of 3D image with a low redundancy framework which possesses the translation- and rotation- invariant subbands. Our experiments on clinical CT and PET datasets have demonstrated that the registration performance, both in terms of speed and accuracy, based on this new multiresolution decomposition has been improved significantly.5. We propose a fast adaptive image interpolation algorithm that classifies pixels and uses different linear interpolation kernels that are adaptive to the class of a pixel. Pixels are classified into regions relevant to the perception of an image, either in a texture region, an edge region, or a smooth region. Image interpolation is performed with Neville filters, which can be efficiently implemented by a lifting scheme. Since linear interpolation tends to over-smooth pixels in edge regions and texture regions, we apply the Laplacian operator to enhance the pixels in those regions. The results of simulations show that the proposed algorithm not only reduces the computational complexity of the process, but also improves the visual quality of the interpolated images.