PRIOR-BASED SEGMENTATION OF MR IMAGES USING GRAPH CUTS

Abstract

The development of faster and higher resolution MR imaging devices has made accessible mass quantities of image data. Much information can be extracted by analyzing these high spatial resolution images. For instance, tissue volumes, which can be measured through MR images, are used as an indicator in many clinical applications and research studies. Research studies involving tissue volume analysis often require the processing of a vast amount of data hence manual segmentation of the images by experts is very time-consuming. Data segmented by human experts are also likely to show inter- and intra-observer inconsistency. For these reasons, automated segmentation of MR images is of great importance and interest.

MR images present many challenges for automated segmentation. In addition to noise, MR images are also corrupted by problems specific to MR imaging such as intensity inhomogeneity. Furthermore, poor contrast at tissue boundaries due to multiple tissues having similar MR intensities also present problems. On the other hand, strong contours may exist where boundaries are not desired, because multiple tissues with very different MR intensities may be present within the same anatomical structure. Prior information, such as spatial atlases and shape priors can be very powerful in these cases.

In this thesis, we developed highly accurate and robust graph cuts-based method that automatically segments MR images. The images we are most interested in are those that cannot be correctly segmented using intensity information alone. We developed models for robustly incorporating prior information such as spatial atlas and geometric or statistical shape priors into the efficient graph cuts segmentation framework. Specifically, we developed methods to incorporate spatial atlas, statistical and geometric shape priors with graph cuts for MR image segmentation. We tested our methods on MR brain, abdomen and cardiac images with intensity inhomogeneity, poor contrast at desired boundaries and/or strong contrast at undesired boundaries and obtained encouraging results. Finally, we proposed a way of dealing with objects with curvy boundaries.