Generalized Scale

Magnetic Resonance (MR) Imaging (MRI), a non-invasive method for imaging the human body, has revolutionized medical imaging. MR image processing, particularly segmentation, and analysis are used extensively in medical and clinical research for advancing our understanding and diagnosis of various human diseases. These efforts face two major difficulties - the first due to image intensity inhomogeneity present as a background variation component, and the second due to the non-standardness of the MR image intensities. Scale is a fundamental concept useful in almost all image processing and analysis tasks. Broadly speaking, scale related work can be divided into multi-scale representations (global models) and local scale models. In this thesis, we present a new morphometric scale model that we refer to as generalized scale which combines the properties of local scale models with the global spirit of multi-scale representations. We contend that this semi-locally adaptive nature of generalized scale confers it certain distinct advantages over other scale formulations, making it readily applicable to solving several image processing tasks.