image segmentation digital image processing

7/30/2019 Image Segmentation Digital Image Processing

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Image Segmentation

CIS 601 Fall 2004

Longin Jan Latecki


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Image Segmentation

Segmentation divides an image into its

constituent regions or objects.

Segmentation of images is a difficult task in

image processing. Still under research.

Segmentation allows to extract objects in

images.

Segmentation is unsupervised learning.

Model based object extraction, e.g.,

template matching, is supervised learning.


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What it is useful for

After a successfulsegmenting the image, the contours of

objects can be extracted using edge detection and/or

border following techniques.

Shape of objects can be described.

Based on shape, texture, and color objects can be

identified.

Image segmentation techniques are extensively used in

similarity searches, e.g.:

http://elib.cs.berkeley.edu/photos/blobworld/
http://elib.cs.berkeley.edu/photos/blobworld/http://elib.cs.berkeley.edu/photos/blobworld/


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Segmentation Algorithms

Segmentation algorithms are based on one oftwo basic properties of color, gray values, ortexture: discontinuity and similarity.

First category is to partition an image based onabrupt changes in intensity, such as edges in animage.

Second category are based on partitioning an

image into regions that are similar according to apredefined criteria. Histogram thresholdingapproach falls under this category.


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Domain spaces

spatial domain (row-column (rc) space)histogram spaces

color space

texture space

other complex feature space


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Clustering in Color Space

1. Each image point is mapped to a point in a color

space, e.g.:

Color(i, j) = (R (i, j), G(i, j), B(i, j))

It is many to one mapping.

2. The points in the color space are grouped to clusters.

3. The clusters are then mapped back to regions in the

image.


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Examples

Mnp: 30, percent 0.05, cluster number 4

Mnp : 20, percent 0.05, cluster number 7

Original pictures segmented pictures


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Displaying objects in the

Segmented Image

The objects can be distinguished by

assigning an arbitrary pixel value or

average pixel value to the pixels belonging

to the same clusters.


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Thus, one needs clustering algorithms

for image segmentation.

Homework 8:

Implement in Matlab and test on some example images

the clustering in the color space.

Use Euclidean distance in RGB color space.You can use k-means, PAM, or some other clustering

algorithm.

Links to k-means, PAM, data normalization

Test images: rose, plane, car, tiger, landscape
http://localhost/var/www/apps/conversion/tmp/DemoProg/kmeans1.mhttp://localhost/var/www/apps/conversion/tmp/DemoProg/K_Medoids.mhttp://localhost/var/www/apps/conversion/tmp/DemoProg/normalize.mhttp://localhost/var/www/apps/conversion/tmp/scratch_5/Images/rose.jpghttp://localhost/var/www/apps/conversion/tmp/scratch_5/Images/plane.jpghttp://localhost/var/www/apps/conversion/tmp/scratch_5/Images/car.jpghttp://localhost/var/www/apps/conversion/tmp/scratch_5/Images/tiger.jpghttp://localhost/var/www/apps/conversion/tmp/scratch_5/Images/landscape.jpghttp://localhost/var/www/apps/conversion/tmp/scratch_5/Images/landscape.jpghttp://localhost/var/www/apps/conversion/tmp/scratch_5/Images/tiger.jpghttp://localhost/var/www/apps/conversion/tmp/scratch_5/Images/car.jpghttp://localhost/var/www/apps/conversion/tmp/scratch_5/Images/plane.jpghttp://localhost/var/www/apps/conversion/tmp/scratch_5/Images/rose.jpghttp://localhost/var/www/apps/conversion/tmp/DemoProg/normalize.mhttp://localhost/var/www/apps/conversion/tmp/DemoProg/K_Medoids.mhttp://localhost/var/www/apps/conversion/tmp/DemoProg/kmeans1.mhttp://localhost/var/www/apps/conversion/tmp/DemoProg/kmeans1.mhttp://localhost/var/www/apps/conversion/tmp/DemoProg/kmeans1.m


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Segmentation by Thresholding

Suppose that the gray-level histogramcorresponds to an image f(x,y) composed ofdark objects on the light background, in such away that object and background pixels havegray levels grouped into two dominant modes.One obvious way to extract the objects from thebackground is to select a threshold T thatseparates these modes.

Then any point (x,y) for which f(x,y) < T is calledan object point, otherwise, the point is called abackground point.


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Gray Scale Image Example

Image of a Finger Print with light background


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Histogram


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Segmented Image

Image after Segmentation


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In Matlab histograms for images can be

constructed using the imhist command.

I = imread('pout.tif');

figure, imshow(I);

figure, imhist(I) %look at the hist to get a threshold, e.g., 110

BW=roicolor(I, 110, 255); % makes a binary image

figure, imshow(BW) % all pixels in (110, 255) will be 1 and white

% the rest is 0 which is black

roicolor returns a region of interest selected as those pixels in Ithatmatch the values in the gray level interval.

BW is a binary image with 1's where the values ofImatch the values

of the interval.


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Thresholding Bimodal Histograms

Basic Global Thresholding:

1)Select an initial estimate for T

2)Segment the image using T. This will produce twogroups of pixels. G1 consisting of all pixels with gray

level values >T and G2 consisting of pixels with values


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Gray Scale Image - bimodal

Image of rice with black background


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Segmented Image

Image after segmentationImage histogram of rice


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Basic Adaptive Thresholding:

Images having uneven illumination makes it difficultto segment using histogram,

this approach is to divide the original image

into sub images

and use the thresholding process

to each of the sub images.


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Multimodal Histogram

If there are three or more dominant modes in theimage histogram, the histogram has to bepartitioned by multiple thresholds.

Multilevel thresholding classifies a point (x,y) asbelonging to one object class

if T1 < (x,y) T2

and to the background

if f(x,y)


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Thresholding multimodal histograms

A method based on

Discrete Curve Evolution

to find thresholds in the histogram.

The histogram is treated as a polyline

and is simplified until a few vertices remain.

Thresholds are determined by vertices that are local

minima.


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Discrete Curve Evolution (DCE)

u

v

wu

v

w

It yields a sequence: P=P0, ..., Pm

Pi+1 is obtained from Pi by deleting the vertices of Pithat have minimal relevance measure

K(v, Pi) = |d(u,v)+d(v,w)-d(u,w)|

>


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Gray Scale Image - Multimodal

Original Image of lena


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Multimodal Histogram

Histogram of lena


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Segmented Image

Image after segmentation we get a outline of her face, hat, shadow etc


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Color Image - bimodal

Colour Image having a bimodal histogram


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Histogram

Histograms for the three colour spaces


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Segmented Image

Segmented image, skin color is shown


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Split and Merge

The goal of Image Segmentation is to findregions that represent objects ormeaningful parts of objects. Major

problems of image segmentation are resultof noise in the image.

An image domain X must be segmented inN different regions R(1),,R(N)

The segmentation rule is a logicalpredicate of the form P(R)


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Introduction

Image segmentation with respect to

predicate P partitions the image X into

subregions R(i), i=1,,N such that

X = i=1,..N U R(i)

R(i) R(j) = 0 for I j

P(R(i)) = TRUE for i = 1,2,,N

P(R(i) U R(j)) = FALSE for i j


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Introduction

The segmentation property is a logical

predicate of the form P(R,x,t)

x is a feature vector associated with region

R

t is a set of parameters (usually

thresholds). A simple segmentation rule

has the form:

P(R) : I(r,c) < T for all (r,c) in R


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Introduction

In the case of color images the feature

vector x can be three RGB image

components (R(r,c),G(r,c),B(r,c))

A simple segmentation rule may have the

form:

P(R) : (R(r,c)


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Region Growing (Merge)

A simple approach to image segmentation

is to start from some pixels (seeds)

representing distinct image regions and to

grow them, until they cover the entireimage

For region growing we need a rule

describing a growth mechanism and a rulechecking the homogeneity of the regions

after each growth step


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Region Growing

The growth mechanism at each stage k

and for each region Ri(k), i = 1,,N, we

check if there are unclassified pixels in the

8-neighbourhood of each pixel of theregion border

Before assigning such a pixel x to a region

Ri(k),we check if the region homogeneity:P(Ri(k) U {x}) = TRUE , is valid


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Region Growing Predicate

The predicate

P: |m(R1)

m(R2)| < k*min{std(R1), std(R2)},is used to decide if the merging

of the two regions R1, R2is allowed, i.e.,

if|m(R1) m(R2)| < k*min{std(R1), std(R2)},

two regions R1, R2are merged.

Rcr

crIn

Rm),(

),(1)(

The arithmetic mean m and standard deviation stdof a

region Rhaving n =|R| pixels:

Rcr

RmcrIn

Rstd),(

2))(),((1

1)(


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Split

The opposite approach to region growing is

region splitting.

It is a top-down approach and it starts with the

assumption that the entire image ishomogeneous

If this is not true, the image is split into four sub

images

This splitting procedure is repeated recursively

until we split the image into homogeneous

regions


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Split

If the original image is square N x N, having

dimensions that are powers of 2(N = 2n):

All regions produced but the splitting algorithm

are squares having dimensions M x M , whereM is a power of 2 as well.

Since the procedure is recursive, it produces an

image representation that can be described by a

tree whose nodes have four sons each

Such a tree is called a Quadtree.


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Split

Quadtree

R0 R1

R2 R3

R0

R1

R00 R01 R02 R04


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Split

Splitting techniques disadvantage, they

create regions that may be adjacent and

homogeneous, but not merged.

Split and Merge method is an iterative

algorithm that includes both splitting and

merging at each iteration:


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Split / Merge

If a region R is inhomogeneous

(P(R)= False) then is split into four sub

regions

If two adjacent regions Ri,Rj are

homogeneous (P(Ri U Rj) = TRUE), they

are merged

The algorithm stops when no furthersplitting or merging is possible


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Split / Merge

The split and merge algorithm produces

more compact regions than the pure

splitting algorithm


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Applications

3D Imaging : A basic task in 3-D image

processing is the segmentation of an image

which classifies voxels/pixels into objects or

groups. 3-D image segmentation makes itpossible to create 3-D rendering for multiple

objects and perform quantitative analysis for the

size, density and other parameters of detected

objects. Several applications in the field of Medicine like

magnetic resonance imaging (MRI).


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Results Region grow


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Results Region Split

R lt R i S lit d


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Results Region Split and

Merge

image segmentation digital image processing

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