On Multi-layer Segmentation Methods For Anatomical MRI Of Human Brain Based On Active Contour Models

The anatomical MRI (aMRI) is able to represent the structure of brain with high accuracy and has become the main medical image for the analysis of human brain. In radiology and neurology, a key problem of aMRI of the brain is how to detect and to measure tissues to represent the anatomy structures of the brain which can be seen as a basic problem of multi-object segmentation for medical images. It is most important for computer-aided diagnosis, quantitative measurement of tissue volumes, localizing foci, anatomy analysis, surgical planning and for surgical navigation. In this thesis, five pyramidal multi-layer frameworks for two-dimensional (2-D) aMRI, vector-valued aMRI and for three-dimensional (3-D) aMRI segmentation were proposed based on the active contour model to deal with the multi-object segmentation of the aMRI.Firstly, a pyramidal multiphase level set framework (or named as pyramidal multiphase C-V model) was developed for multi-object segmentation of 2-D anatomical MRI by combining a key technique, named as the technique of painting background with the Chan-Vese model which was able to deal with the limitation of the C-V model in multi-object segmentation and in multiple junction representation and was especially suitable for images with multiple sub-objects.Secondly, to reduce the difference between the results obtain by the proposed pyramidal model and manual segmentation results, a generalized technique of painting background was proposed by using a variable painting color. An improved pyramidal model using the generalized technique of painting background and the C-V model was proposed which was able to adjust the boundaries of sub-objects and can be used for interactive segmentation of the aMRI.Thirdly, to partition the tissues with close relaxation time, a vector-valued pyramidal multiphase C-V model using the different sensitivities of different kinds of medical images or different MR images obtained by different serials was developed.Fourthly, a voxel-based pyramidal model was developed for 3-D aMRI segmentation by extending the level set function to three dimensional.Fifthly, by combining the simultaneous multiphase C-V model and the pyramidal model, a pyramidal simultaneous multiphase C-V model was proposed which was able to partition a given image according to a complex segmentation tree.Experimental results show that the proposed algorithms are able to detect multi-objects with high accuracy in 2-D aMRI, vector-valued aMRI and 3-D aMRI. Also, they can reduce the difference between the obtained segmentation results and the mammul segmentation results by using interactive segmentation and are suitable for multi-object segmentation of medical images whose object regions contain sub-objects.