Eigenvector-based Registration And Segmentation For Brain CT Image

With the rapid development of medical imaging technology, some advanced imaging equipments, such as Multi-slice Spiral CT, are clinically widely used. The volume data of high quality and large quantity, which is produced by these equipments, can provide the doctors with clearer medical images, while at the same time, also put heavy reading burden on them. The Computer Aided Diagnosis (CAD) system can be an effective means to solve the problem.The high resolution CT (HRCT) image is an important substitute for MRI image in some occasions where MRI cannot be used. In particular, the HRCT image has a unique advantage for the detection of brain tumors and cerebral hemorrhage. In order to perform the research of the CT image-based Computer Aided Diagnosis (CAD), many related algorithms, like feature extraction, image registration and image segmentation should be designed and studied.In this paper, the local histogram-based Geometrical Moment Invariants (GMI) is proposed on the basis of medical image texture analysis algorithm. Eigenvector is constructed by both calculating the moments based on the local histogram in different scale regions of interest (ROI) and incorporating the boundary information of every pixel. Eigenvector can be regarded as the morphology signature of the pixel. The experiment results show that the eigenvectors, mapping the information of CT image from intensity space into feature space, aggrandize both the similarity of the pixels in the same tissue and discrepancy of the pixels belonging to different tissues, thus demonstrate its good performance in discriminating the medical image texture feature.The non-rigid registration is a key process in the CAD system. An eigenvector-based corresponding point automatic detection algorithm is proposed to determine the corresponding landmark points between moving images and fixed images automatically and correctly. The algorithm successfully solved the problem that corresponding point must be labeled manually in the point-based non-rigid registration. Furthermore, this advantage is used in the Approximating Thin-Plate Splines (ATPS) scheme to design an eigenvector-based non-rigid registration algorithm for brain CT images. The registration algorithm can guarantee the one-to-one mapping between the corresponding points in the key anatomical positions and can be applied for the registration of both normal medical images and the abnormal ones.In the Atlas-based pathology automatic detection, image segmentation is an important quantitatively analysis method. Eigenvectors can discriminate the pixels of which the intensities are same or similar but belong to different tissues. Therefore, they can be classified by Modified Fuzzy C-mean Method (MFCM) in the feature space and the classification result will be mapped into intensity space. The cerebrum area on brain CT images, in this way, can be segmented into white matter (WM), gray matter (GM) and cerebrospinal fluid (CSF).In this research, the algorithms of texture feature extraction, medical non-rigid registration and medical segmentation has been realized. These algorithms can be applied in the Computer-Aided Diagnosis System, so to some extent, they are valuable.This research was sponsored by Nature and Science Foundation of China (Project No. 60771007).