Bio-medical Ontologies Maintenance and Change Management

Multimedia Medical Databases 121
many biomedical imaging applications: the quantification of tissue volumes, diagnosis,
localization of pathology, study of anatomical structure, treatment planning,
partial volume correction of functional imaging data, and computer-integrated
surgery [76, 81].
Segmentation is a difficult task because in most cases it is very hard to separate
the object from the image background. Also, the image acquisition process brings
noise in the medical data. Moreover, inhomogeneities in the data might lead to
undesired boundaries. The medical experts can overcome these problems and
identify objects in the data due to their knowledge about typical shape and image
data characteristics. But, manual segmentation is a very time-consuming process
for the already increasing amount of medical images. As a result, reliable automatic
methods for image segmentation are necessary [76, 81].
As in content-based visual retrieval on color or texture features, it cannot be
said that there is a segmentation method for medical images that produces good
results for all types of images. There have been studied several segmentation
methods that are influenced by factors like: application domain, imaging modality
or others [76, 67].
Image segmentation is defined as the partitioning of an image into no overlapping,
constituent regions that are homogeneous, taking into consideration some
characteristic such as intensity or texture [76].
If the domain of the image is given by I , then the segmentation problem is to
determine the sets Sk ⊂ I whose union is the entire image . Thus, the sets that make
up segmentation must satisfy
(5.1)
where S k ∩ S j = ∅ for k≠j and each S k is connected [76, 86].
In an ideal mode, a segmentation method finds those sets that correspond to
distinct anatomical structures or regions of interest in the image.
When the constraint from the above definition is removed, then the operation of
determining the sets is called pixel classification and the sets are called classes
[76]. Pixel classification can be very important in medical application, especially
when disconnected regions belonging to the same tissue class need to be identified.
The determination of the total number of classes in pixel classification can be
also a difficult problem.
Another process bounded by segmentation is called labeling, that means assigning
a meaningful designation to each region or class [76]. It can be performed
separately from segmentation. Labeling process maps the numerical index k of set
S k , to an anatomical designation. In medical imaging, the labels are often visually
obvious and can be determined upon inspection by a medical expert. Computer
automated labeling is desirable when labels are not obvious, or in automated processing
systems. This situation occurs in digital mammography where the image is
segmented into distinct regions and the regions are subsequently labeled as being
healthy tissue or with tumor.