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.

Autorentext

Dr. Anant Madabhushi is an Assistant Professor and Director of the Laboratory for Computational Imaging and Bioinformatics (LCIB), Dept. of Biomedical Engineering, Rutgers University. Dr. Madabhushi has written over 50 peer reviewed papers in the areas of medical image analysis, computer-aided diagnosis, computer vision, and machine learning.

Klappentext
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.