Image segmentation techniques

Submitted by,

G. Midhu Bala and J.Asenath

Introduction Image processing is any form of signal processing for

which the input is an image, such as a photography or videoframe.

The output of image processing may be either an image or aset of characteristics related to the image.

Image Analysis - to extract high level information on animage.

Image Segmentation - to change the representation of anoriginal image into meaningful portions which makes iteasier to analysis.

To locate objects and boundaries.

Segmentation Techniques

•Thresholding

•Edge Detection

•Color Image Segmentation

•Histogram Based Method

Types of images8 bit image

8 bit image RGB

16 bit image

16 bit RGB

32 bit image

32 bit RGB

8 bit Color

8 bit color RGB

RGB Color

RGB Stack

Red Green Blue

File formats

JPG uses lossy compression

GIF always uses lossless LZW compression, but it is always an

indexed color file (8-bits, 256 colors maximum), which is poor for

24-bit color photos.

PNG is transparency for 24 bit RGB images. lossless

compression, of different types), but PNG is perhaps slightly slower

to read or write.

TIF is lossless (including LZW compression option), which is

considered the highest quality format for commercial work.

Test image

.

Original disease free leaf Original affected leaf

Thresholding

Original image into binary image

Foreground can be separated from the background by selecting the

threshold value

Global Thresholding -only one threshold value for entire image

Local Thresholding - different value for different regions

Methods

Edge Based - detects and links edge pixels to form contour.

Region Based - detects the entire region

Threshold Value : 100 Threshold Value : 150 Threshold Value : 255

Global Thresholding

Otsu Method

Threshold Value : 193

Edge Detection

Reduce the amount of data in an image.

Provides ability to extract the exact edge.

Corners, lines, curves .

Meaningful discontinuities in the grey level.

Edge detected image

Canny Edge Detection:(Criteria)

Detection: The probability of detecting real edge

points is maximized and falsely detecting non-edge

points is minimized. This corresponds to maximizing the

signal-to-noise ratio.

Localization: The detected edges should be as close as

possible to the real edges.

Number of Responses: One real edge should be result

in more than one detected edge.

Canny Edge Detection Algorithm

Smoothing:

Blurring of the image to remove noise.

Finding gradients:

The edges should be marked where the gradients of the

image has large magnitudes.

Non-maximum suppression:

Only local maxima should be marked as edges.

Double thresholding :

Potential edges are determines by thresholding.

Edge tracking by hysteresis:

Final edges are determined by suppressing all edges that are

not connected to a very certain strong edges.

Color Image Segmentation:Color image segmentation is used to extract high level

information of the image based on color. Three phases are

Phase1: Preprocessing:

Morphological methods are applied to remove the noises away

from image which applied to smooth some spots on uniformed

patterns.

Phase2: Transformation:

Color space transformed methods are used to transform other

color space to RGB.

Phase3: Segmentation:

Applying clustering algorithm like K-means algorithm for

finding the appropriate cluster numbers and segment images in

different color spaces. The cluster with the maximum average

variance is split into new clusters.

Segmented image

Histogram- based methods: Compute- Pixels , peaks, valleys

Locate – clusters

Recursively applied for finding the smaller clusters.

Distinguishes the two homogeneous regions of the

foreground and background of an image.

Histogram

Conclusion

Partitioning an Image using segmentation

techniques leads to extract different regions with

similar attributes . It also detects high level

information of an image for image analysis and

further researches.