Medical Image Segmentation on Multimodality Images

摘要

Segmentation is a hot issue in the domain of medical image analysis. It has a wide range of applications on medical research. A great many medical image segmentation algorithms have been proposed, and many good segmentation results were obtained. However, due to the noise, density inhomogenity, partial volume effects, and density overlap between normal and abnormal tissues in medical images, the segmentation accuracy and robustness of some state-of-the-art methods still have room for improvement. This thesis aims to deal with the above segmentation problems and improve the segmentation accuracy. This project investigated medical image segmentation methods across a range of modalities and clinical applications, covering magnetic resonance imaging (MRI) in brain tissue segmentation, MRI based multiple sclerosis (MS) lesions segmentation, histology based cell nuclei segmentation, and positron emission tomography (PET) based tumour detection. For the brain MRI tissue segmentation, a method based on mutual information was developed to estimate the number of brain tissue groups. Then a unsupervised segmentation method was proposed to segment the brain tissues. For the MS lesions segmentation, 2D/3D joint histogram modelling were proposed to model the grey level distribution of MS lesions in multimodality MRI. For the PET segmentation of the head and neck tumours, two hierarchical methods based on improved active contour/surface modelling were proposed to segment the tumours in PET volumes. For the histology based cell nuclei segmentation, a novel unsupervised segmentation based on adaptive active contour modelling driven by morphology initialization was proposed to segment the cell nuclei. Then the segmentation results were further processed for subtypes classification. Among these segmentation approaches, a number of techniques (such as modified bias field fuzzy c-means clustering, multiimage spatially joint histogram representation, and convex optimisation of deformable model, etc.) were developed to deal with the key problems in medical image segmentation. Experiments show that the novel methods in this thesis have great potential for various image segmentation scenarios and can obtain more accurate and robust segmentation results than some state-of-the-art methods.