Tumor Segmentation From MRI Image And Epilepsia Detection From EEG

This paper comes from the Sino-French joint training doctoral students'project. The research mainly involves two aspects:MRI brain tumor segmentation and detection of EEG epileptic brain.First, the magnetic resonance image segmentation research in brain tumor research focuses on the following aspects.Accurate and robust brain tissue segmentation is a very important issue for the diagnosis and treatment of brain tumors and the study of some brain disorders. One example is to analyze and estimate quantitatively the growth process of brain tumors, and to evaluate effects of some pharmaceutical treatments in clinic. Once a tumor is found, physicians can measure various quantities, such as the size and the location of tumors. However, tracing a tumor in 3D manually by an expert is not only exceedingly time consuming, but also exhausting for the expert leading to human errors. Therefore, it is necessary to develop segmentation tools with minimum manual intervention.Automatic, accurate and robust brain tissue, and brain tumor segmentation is a great challenging task because it usually involves a large amount of data with sometimes artifacts due to patient's motion or limited acquisition time and soft tissue boundaries. In addition there is a large class of tumor types which have a variety of shapes and sizes, and may appear at any location and in different image intensities. Some of them may also deform the surrounding brain structures. The existence of several MR acquisition protocols can provide different information on the brain. Each image usually highlights a particular region of the tumor. Thus, automated segmentation with prior models or using prior knowledge is difficult to implement.In this context, the aim of our project is to develop a framework for an automatic, robust and accurate segmentation of a large class of brain tumors in MR images. The built system based on this framework is used to follow a specific patient in his whole therapeutic period while his MRI images acquired once every four months a year, allowing the clinician to monitor tumor developing states and evaluating the therapeutic treatment.The framework consists of three steps:image preprocessing, tumor segmentation and result comparison and therapy evaluation.Image preprocessing. In this step, operations such as:reduction of intensity inhomogeneity and inter-slice intensity variation of images, spatial registration (alignment) of the input images are performed. This section prepares images and some global information on the brain to be used in the segmentation section. Tumor segmentation. First, the approximate symmetry plane of the MRI volume is computed, and the initial contour of the tumor, if the tumor is present in the image, is searched by utilizing the symmetry plan information. Second, a level set method is used to refine the initial contour to get the tumor boundary. Last, the tumor boundary is, in the middle part of the MRI volume in general, projected to its adjacent slices for the new initial contours of the adjacent slices. The same refinement algorithm is applied to get all tumor boundaries through the whole volume. All the boundaries in the same volume are used to reconstruct 3D tumor volume for the tumor quantitative measurements.Result comparison and therapy evaluation. In this last step, by following up the tumor variations in the therapeutic period, the clinician can carry out comparison studies according to the medical requirement, and give the evaluation of the therapeutic treatment.Experimentation and validation results show that the proposed segmentation approach has the ability to segment MRI volumes automatically, and has a relatively good segmentation effect; Experimentations also show that the segmentation results are not too sensitive to the parameters in level set evolution. The built system based on this framework is used to follow a specific patient in his whole therapeutic period while his MRI images acquired once every four months a year, allowing the clinician to monitor tumor developing states and evaluating the therapeutic treatment.Second, EEG analysis and brain seizure detection work mainly involves the following aspects.In recent years, researchers have been using a variety of signal analysis and processing techniques, try to design for automatic EEG diagnostics. Time-frequency analysis of these methods are developing fast, its full consideration of the characteristics of EEG non-stationary in time-frequency plane, time-varying characteristics of the signal, it can be with good resolution in time and frequency. Although the time-frequency analysis method has these advantages, however, when using it for EEG analysis, it is often accompanied by overlapping of different frequencies generated by the cross-term, resulting in a false judgment.In this thesis, combined with time-frequency analysis method and singular value decomposition method, the impact of the cross-term is diminished, and different time-frequency distribution measurement methods are tried to detect cerebral epileptic signal to obtain good results. To further suppress cross terms, the empirical mode decomposition and reconstruction method are used, because the reconstruction takes into account the characteristics of EEG, it can be expected to inhibit the detection of the signal components of other components, so it can better achieve the purpose of cross-term suppression. This is the way to achieve better detection results of brain seizures. The main contributions of this research work discussed in the dissertation mainly include:(1) Introduction of a mid-sagittal plan estimation algorithm.As to axial MRI images, the symmetry plane of a normal brain is a good approximation of the mid-sagittal plane, best separating the hemispheres. To determine the location of the plane, we compute a degree of similarity between the slice image and its reflection with respect to a plane, by utilizing each slice and combining results from multiple slices. The best plane is then obtained by maximizing the similarity measure.(2) Proposition of an initial contour seeking algorithm.After the extraction of the mid-sagittal plane, we then calculate the differences between two hemispheres. The slice with the largest difference is checked out. Using a combination of watershed and morphology algorithms, the region without symmetry can be determined, which is considered as the initial contour of the tumor in this slice. Usually it is the largest size contour of the tumor.(3) Proposition of an improved level set formulation based on active contour model.To refine the initial contour obtained in the above step, which is not accurate enough, we use edge information. An improved level set formulation based on active contour model is applied for this purpose. The proposed method tries to combine region and edge information, thus taking advantage of both approaches while cancelling their drawbacks.(4) Implement software to perform 3D data comparison.After all the tumor data of the volumes in the therapeutic period have been segmented, a 3D reconstruction algorithm is designed to visualize the tumor and quantify the tumor information making it convenient for the clinician evaluate the therapeutic treatment.(5) Proposition of a time-frequency distribution based on singular value decomposition method of cross-term reduction approach.(6) Proposition of a time-frequency analysis based on empirical mode decomposition method of cross-term suppression approach.