Bio-medical Ontologies Maintenance and Change Management

122 L. Stanescu, D. Dan Burdescu, and M. Brezovan
The segmentation methods can operate in a 2-D image domain or a 3-D image
domain [32, 33, 53, 54, 76]. This property is called dimensionality. Methods based
only on image intensities are independent of the image domain. Certain methods
such as deformable models, Markov random fields, and region growing, incorporate
spatial information may therefore operate differently depending on the dimensionality
of the image.
The segmentation methods were grouped in the following categories: thresholding,
region growing, classifiers, clustering, Markov random field models,
artificial neural networks and deformable models. Of course, there are other important
methods that do not belong to any of these categories [76, 81].
In thresholding approaches an intensity value called the threshold must be established.
This value will separate the image intensities in two classes: all pixels
with intensity greater than the threshold are grouped into one class and all the
other pixels into another class. As a result, a binary partitioning of the image intensities
is created. If more than one threshold is determined, the process is called
multi-thresholding [76].
Thresholding is often used as the first step in a sequence of image processing
operations. It has some limitations:
• In its simplest form only two classes are generated
• It cannot be applied to multi-channel images
• Typically, does not take into account the spatial characteristics of the image.
Region growing is a technique for extracting a region from a image that contains
pixels connected by some predefined criteria, based on intensity information
and/or edges in the image. In its simplest form, region growing requires a seed
point that is manually selected by an operator, and extracts all pixels connected to
the initial seed having the same intensity value [47, 76].
Like thresholding, region growing is not often used alone. It can be used particularly
for emphasizing small and simple structures such as tumors and lesions.
Limitations:
• It requires manual interaction to obtain the seed point
• Can also be sensitive to noise, causing extracted regions to have holes or even
become disconnected
Split and merge algorithms are related to region growing but do not require a
seed point.
Classifier methods represent pattern recognition techniques that try to partition
a feature space extracted from the image using data with known labels. A feature
space is the range space of any function of the image, with the most common feature
space being the image intensities themselves. Classifiers are known as supervised
methods because they need training data that are manually segmented by
medical experts and then used as references for automatically segmenting new
data [76].