Introduction
to Image Segmentation Technique
Image Segmentation
Image segmentation is the process of
partitioning a digital image into multiple segments. The main aim of segmentation
is simplification. Representing an image into meaningful and easily analyzable
way. The goal of image segmentation is to divide
an image into multiple segments based on similar features or attributes. Image
segmentation is used to locate object and boundaries in images.
Segmentation
algorithm based on of two basic properties of gray-scale values:
1. Discontinuity: This
is the approach in which an image is segmented into regions based on
discontinuity. It partition an image based on abrupt changes in gray scale
levels. The
edge detection based segmentation belong in this category in which edges formed
due to intensity discontinuity are detected and linked to form boundaries of
regions.
2. Similarity: The
techniques that based on this approach are: Thresholding techniques, region
growing techniques and region splitting and merging. These all divide the image
into regions having similar set of pixels.
In other words,
also we can say that image segmentation can be approached from three
perspectives: Region approach, Edge approach and Data clustering. The region
approach falls under similarity detection and edge detection and boundary
detection falls under discontinuity detection. Clustering techniques are also
under similarity detection
We can partition ta image into various parts
also called segments. After dividing an image into segments, instead of processing the entire image you can process only the important
segments of the image.
The Different Types of
Image Segmentation
1.
Thresholding Method
2.
Region-based Segmentation
3.
Edge Detection Segmentation
4.
Image Segmentation based on Clustering
1. Thresholding Method
In this method an
image is partitioned into two or more sub-images upon comparing with the
predefined threshold value. These methods divide the image pixel accordingly to
their intensity level. These methods are used over images having lighter
objects than background. Suppose that an image, f(x, y), is composed of light
objects on a dark background, and the following figure is the histogram of the
image.
Then, the objects can be extracted by comparing pixel values with a threshold T. One way to extract the objects from the background is to select a threshold T that separates object from background. Any point (x, y) for which f(x, y) > T is called an object point; otherwise the point is called a background point.g (x, y) = 〘 1 if f (x, y) > T f
0 f (x, y) < = T
When T is a constant applicable over an entire image, then the above process is called as Global thresholding.
When the value of T changes over an image then that process is referred as Variable thresholding. Sometimes it is also termed as local or regional thresholding. Where, the value of T at any point (x ,y) in an image depends on properties of a neighborhood of (x, y) .If T depends on the spatial coordinates (x ,y) themselves, then variable thresholding is often referred to as dynamic or adaptive thresholding.
2.
Region-based
Segmentation
Region-based segmentation
divide an image into regions. This method work on the principle of homogeneity.
This technique partition an image into several regions based on criteria like
color, intensity, object.
Types of region
based:
1.
Region Growing - Region growing
is a procedure that groups pixel or sub-regions into larger regions. It starts
with a set of “seed” points and from these grows regions by appending to each
seed points those neighboring pixels that have similar properties (such as gray
level, texture, color, shape).In noisy image edges are extremely difficult to
detect. For noisy image region growing is better. Homogeneity of regions is
most important in segmentation. There are several criteria for homogeneity:
gray level, color, texture, shape, model etc.
2. Region Splitting- An
alternative is to start with the whole image as a single region and subdivide
the regions that do not fulfill a condition of homogeneity.
3. Region
Merging- Region
merging is the opposite of region splitting. Start with small regions like 2*2
or 4*4 regions and marge the regions that have similar characteristics. Splitting
and merging approaches are used iteratively.
4. Split and Merge with Quad Trees
Initialize segmentation
is arbitrarily or using prior knowledge.
For each region R, if it
is not homogeneous, split it. This builds a quad tree.
If R1 and R2 are
neighbors, and they can be merged into a homogeneous region do it. This might
destroy the quad tree structure, because you might get a1/4 region connected to
a 1/6 region.
If any regions are too
small merge them with the best neighbor.
Quad tree
1.
Split into 4 disjoint quadrants any region Ri for which P(Ri) = FALSE
2.
Merge any adjacent region Rj and Rk for
which
P (Ri <= Rk ) = TRUE
Stop when no further merging or splitting is
possible.
3. Edge Detection Segmentation
Edge detection is an image
processing technique for detecting the boundaries of objects. It
works by finding discontinuities
in brightness. In this method first of all the edges are detected and
then are connected together to form the object boundaries to segment the
required regions.
This methods
transform original images into edge images. It is a fundamental process detects and
outlines of an object and boundaries among objects and the background in the
image. Edge detection is the most familiar approach for detecting significant
discontinuities in intensity values. A single intensity value does not provide
good information about edges. Edge detection techniques locate the edges where
either the first derivative of intensity is greater than a particular threshold
or the second derivative has zero crossings.
There are two
basic methods first is Gray histograms based method and second is Gradient
based methods. For detecting edges there are several techniques are used like
canny operator, sobel operator and Robert’s operator etc. Resulting images will
be binary image. These are the structural techniques based on discontinuity
detection.
Image Segmentation
based on Clustering
Clustering is
unsupervised learning method. It deals with finding a structure in a collection
of unlabeled data. Clustering is the process of organizing objects into groups accordingly
similar properties of object. Similar cluster are group together.
There are several methods and one of the most popular methods is K-Means clustering algorithm.
K-means algorithm is
simple and easy to understand. For small set k-means are very suitable. This is an unsupervised algorithm. It is used to
segment the interest area from the background. Based on the K-centroids it
clusters or partitions the given data into K-clusters or parts. We
assign the points to the clusters which are closest to them. K-means is a
distance based algorithm.
Steps of
K-Means algorithm:
1. Choose the
number of clusters K.
2. Select at random
K points, the centroids (not necessarily from your dataset).
3. Assign each data
point to the closest centroid → that forms K clusters.
4. Compute and
place the new centroid of each cluster.
5. Reassign each
data point to the new closest centroid. If any reassignment took place, go to
step (4), otherwise, the model is ready.
Applications
1. Medical Images i.e. finding tumors veins, etc.
2. Satellite/Aerial images i.e. finding targets
3. Object Detection i.e. face recognize,
fingerprint recognize
4. Surveillance Images
CONCLUSION
Image segmentation
is one of the most important method. Segmentation is used to partition an image
into several disjoint subsets called segments. Each segment corresponds to a
meaningful part of the image. Image segmentation is a difficult task since
often the scene objects are defined by image regions with non-homogenous
texture and color characteristics. Although a variety of image segmentation
methods have been developed, there is still no general method suitable for
segmenting any type of images and thus research on image segmentation method
has become the key issue. Therefore, further research is required to develop an
effective segmentation technique to partition an image for locating and
identifying objects or boundaries in an image in a clear and meaningful way to
meet the demands of advanced applications.
These techniques can be used for
object recognition and detection. In medical images these can be used to detect
cancer and in satellite images these can be used to detect roads and bridges.
Thus it is clear that various methods are suitable for various types of image
applications. But from the study it is clear that no single method is
sufficient for every image type and no all methods are suitable for a
particular image type. Due to the need of image segmentation in many applications,
it has a challenging future.