Biomedical Engineering Reference
In-Depth Information
Chapter 2
State of the Art of Level Set Methods in
Segmentation and Registration of Medical
Imaging Modalities
Elsa Angelini,
1
Yinpeng Jin,
1
and Andrew Laine
1
2.1
Introduction
Segmentation of medical images is an important step in various applications
such as visualization, quantitative analysis and image-guided surgery. Numer-
ous segmentation methods have been developed in the past two decades
for extraction of organ contours on medical images. Low-level segmentation
methods, such as pixel-based clustering, region growing, and filter-based edge
detection, require additional pre-processing and post-processing as well as con-
siderable amounts of expert intervention or information of the objects of inter-
est. Furthermore, the subsequent analysis of segmented objects is hampered
by the primitive, pixel or voxel level representations from those region-based
segmentation [1].
Deformable models, on the other hand, provide an explicit representation of
the boundary and the shape of the object. They combine several desirable fea-
tures such as inherent connectivity and smoothness, which counteract noise and
boundary irregularities, as well as the ability to incorporate knowledge about
the object of interest [2, 3, 4]. However, parametric deformable models have two
main limitations. First, in situations where the initial model and desired object
boundary differ greatly in size and shape, the model must be reparameterized
dynamically to faithfully recover the object boundary. The second limitation
