Fundamentals of image segmentation - Biomedical Engineering Desk Reference - page 564

and assign it to the cluster that best represents the value of
its characteristic vector of features of interest. Region
growing is another class of region segmentation algorithms
that assign adjacent pixels or regions to the same segment
if their image values are close enough, according to some
preselected criterion of closeness [77, 85] .
The strategy of edge-based segmentation algorithms is
to find object boundaries and segment regions enclosed
by the boundaries [16, 36, 41, 72, 96] . These algorithms
usually operate on edge magnitude and/or phase images
produced by an edge operator suited to the expected
characteristics of the image. For example, most gradient
operators such as Prewitt, Kirsch, or Roberts operators
are based on the existence of an ideal step edge. Other
edge-based segmentation techniques are graph searching
and contour following [6, 14, 106] .
Traditionally, most image segmentation techniques use
one type of images (MR, CT, PET, SPECT, ultrasound,
etc.). However, the performance of these techniques can
be improved by combining images from several sources
( multi-spectral segmentation [29, 89, 117] ) or integrating
images over time ( dynamic or temporal segmentation [71,
93, 108] ).
The following sections will present some of the seg-
mentation techniques that are commonly used in medical
imaging. In Section 6.4.2 we will discuss several thresh-
olding techniques. Section 6.4.3 will describe region
growing techniques. The watershed algorithm will be
reviewed in Section 6.4.4. Section 6.4.5 will present
edge-based segmentation techniques. A discussion of
multispectral segmentation methods will be given in
Section 6.4.6.

Figure 6.4-1 An example of bimodal histogram with selected
threshold T.

operation that compares image values with a threshold
value T [25, 107] . Suppose that we have an image f ( x , y )
with the histogram shown in Fig. 6.4-1 .
The object and background pixels have gray levels
grouped into two dominant modes. One obvious way to
extract the object from the background is to select
a threshold T that separates these modes.
The thresholded image g ( x , y ) is defined as

gðx; yÞ¼ 1 ðx; yÞ > T
0 ðx; yÞT

(6.4.1)

The result of thresholding is a binary image, where pixels
with intensity value of 1 correspond to objects, while
pixels with value 0 correspond to the background.
Figure 6.4-2 shows the result of segmentation by
thresholding. The original image ( Fig. 6.4-2 A) contains
white cells on a black background. Pixel intensities vary
between 0 and 255. The threshold T ¼ 127 was selected
as the minimum between two modes on a histogram
( Fig. 6.4-2 B), and the result of segmentation is shown in
Fig. 6.4-2 C, where pixels with intensity values higher
than 127 are shown in white. In the last step ( Fig. 6.4-2 D)
the edges of the cells were obtained by a 3 3 Laplacian
(second-order derivative [36] ; also see description in
Section 6.4.5), which was applied to the thresholded
image in Fig. 6.4-2 C.
There are many other ways to select a global thresh-
old. One of them is based on a classification model that
minimizes the probability of error [77] . For example, if
we have an image with a bimodal histogram (e.g., object
and background), we can calculate the error as the total
number of background pixels misclassified as object
and object pixels miscalssified as background. A semi-
automated version of this technique was applied by
Johnson et al. [56] to measure ventricular volumes from
3D magnetic resonance (MR) images. In their method an
operator selects two pixelsdone inside an object and one
in the background. By comparing the distribution of pixel

6.4.2 Thresholding

Several thresholding techniques have been developed
[16, 25, 36, 41, 51, 96-98, 107, 127] . Some of them are
based on the image histogram; others are based on local
properties, such as local mean value and standard de-
viation, or the local gradient. The most intuitive approach
is global thresholding. When only one threshold is se-
lected for the entire image, based on the image histo-
gram, thresholding is called global. If the threshold
depends on local properties of some image regions, for
example local average gray value, thresholding is called
local. If the local thresholds are selected independently
for each pixel (or groups of pixels), thresholding is called
dynamic or adaptive.

6.4.2.1 Global thresholding

Global thresholding is based on the assumption that the
image has a bimodal histogram and, therefore, the object
can be extracted from the background by a simple