Segmentation (Image Processing) - Important Concepts in Signal Processing, Image Processing and Data Compression - page 34

computer vision and medical image analysis. Furthermore, research into various level set
data structures has led to very efficient implementations of this method.
Graph partitioning methods
Graph partitioning methods can effectively be used for image segmentation. In these
methods, the image is modeled as a weighted, undirected graph. Usually a pixel or a
group of pixels are associated with nodes and edge weights define the (dis)similarity
between the neighborhood pixels. The graph (image) is then partitioned according to a
criterion designed to model "good" clusters. Each partition of the nodes (pixels) output
from these algorithms are considered an object segment in the image. Some popular
algorithms of this category are normalized cuts , random walker , minimum cut ,
isoperimetric partitioning and minimum spanning tree-based segmentation .
Watershed transformation
The watershed transformation considers the gradient magnitude of an image as a
topographic surface. Pixels having the highest gradient magnitude intensities (GMIs)
correspond to watershed lines, which represent the region boundaries. Water placed on
any pixel enclosed by a common watershed line flows downhill to a common local
intensity minimum (LIM). Pixels draining to a common minimum form a catch basin,
which represents a segment.
Model based segmentation
The central assumption of such an approach is that structures of interest/organs have a
repetitive form of geometry. Therefore, one can seek for a probabilistic model towards
explaining the variation of the shape of the organ and then when segmenting an image
impose constraints using this model as prior. Such a task involves (i) registration of the
training examples to a common pose, (ii) probabilistic representation of the variation of
the registered samples, and (iii) statistical inference between the model and the image.
State of the art methods in the literature for knowledge-based segmentation involve active
shape and appearance models, active contours and deformable templates and level-set
based methods.
Multi-scale segmentation
Image segmentations are computed at multiple scales in scale-space and sometimes
propagated from coarse to fine scales.
Segmentation criteria can be arbitrarily complex and may take into account global as well
as local criteria. A common requirement is that each region must be connected in some
sense.