Biomedical Engineering Reference
In-Depth Information
6.4
Chapter 6.4
Fundamentals of image
segmentation
Jadwiga Rogowska
6.4.1 Introduction
varies according to the goal of the study and the type of the
image data. Different assumptions about the nature of the
analyzed images lead to the use of different algorithms.
Segmentation techniques can be divided into classes in
many ways, depending on classification scheme:
The principal goal of the segmentation process is to
partition an image into regions (also called classes, or
subsets) that are homogeneous with respect to one or
more characteristics or features [11, 16, 20, 30, 36, 66,
77, 96, 107, 109] .Segmentationisanimportanttoolin
medical image processing and it has been useful in many
applications. The applications include detection of the
coronary border in angiograms, multiple sclerosis lesion
quantification, surgery simulations, surgical planning,
measuring tumor volume and its response to therapy,
functional mapping, automated classification of blood
cells, studying brain development, detection of micro-
calcifications on mammograms, image registration,
atlas-matching, heart image extraction from cardiac
cineangiograms, detection of tumors, etc. [8, 14, 15, 35,
38, 41a, 61, 71, 88, 109, 115, 132] .
In medical imaging, segmentation is important for
feature extraction, image measurements, and image dis-
play. In some applications it may be useful to classify
image pixels into anatomical regions, such as bones,
muscles, and blood vessels, while in others into patho-
logical regions, such as cancer, tissue deformities, and
multiple sclerosis lesions. In some studies the goal is to
divide the entire image into subregions such as the white
matter, gray matter, and cerebrospinal fluid spaces of the
brain [67] , while in others one specific structure has to be
extracted, for example breast tumors from magnetic
resonance images [71] .
A wide variety of segmentation techniques has been
proposed (see surveys in [11, 20, 30, 41, 77, 83, 127] ).
However, there is no one standard segmentation tech-
nique that can produce satisfactory results for all imaging
applications. The definition of the goal of segmentation
Manual, semiautomatic, and automatic [101] .
Pixel-based (local methods) and region-based (global
methods) [4] .
Manual delineation, low-level segmentation
(thresholding, region growing, etc.), and model-
based segmentation (multispectral or feature map
techniques, dynamic programming, contour
following, etc.) [109] .
Classical (thresholding, edge-based, and
region-based techniques), statistical, fuzzy, and
neural network techniques [87] .
The most commonly used segmentation techniques
can be classified into two broad categories: (1) region
segmentation techniques that look for the regions satis-
fying a given homogeneity criterion, and (2) edge-based
segmentation techniques that look for edges between re-
gions with different characteristics [ 16, 36, 77, 96, 107].
Thresholding is a common region segmentation
method [25, 83, 98, 107, 127] . In this technique a
threshold is selected and an image is divided into groups
of pixels having values less than the threshold and groups
of pixels with values greater or equal to the threshold.
There are several thresholding methods: global methods
based on gray-level histograms, global methods based on
local properties, local threshold selection, and dynamic
thresholding. Clustering algorithms achieve region seg-
mentation [13, 27, 37, 54] by partitioning the image into
sets or clusters of pixels that have strong similarity in the
feature space. The basic operation is to examine each pixel
Search WWH ::




Custom Search