Image SegmentationRegion growing & Contour following

Hyeun-gu Choi

Advisor: Dr. Harvey RhodyCenter for Imaging Science

5.8.98 Hyeun-gu Choi

Background

• Definition of image segmentation– Subdivides an image into its constituent parts

or objects.

• Region growing– Based on similarity of gray-level values

• Contour following– Based on discontinuity of gray-level values

5.8.98 Hyeun-gu Choi

Detection of discontinuity

• Gradient operators - First derivative(Sobel)– Zero value - constant gray level– Positive value - transition from dark region to

light region– Negative value - transition from light region

to dark region

121

000

121

101

202

101

5.8.98 Hyeun-gu Choi

Detection of discontinuity

• Gradient operators - Second derivative (Laplacian)

– Positive value - dark region– Negative value - light region– This operator is good for deciding whether a

pixel is on the dark or light side of an edge.

010

141

010

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• Threshold Selection Based on Boundary Characteristics

Application of these operators(1)

T: Threshold values(x,y): result image

5.8.98 Hyeun-gu Choi

Examples of derivative operator

Original image with background picture

Segmented image without background by Sobel and Laplacian operator

5.8.98 Hyeun-gu Choi

Results of derivative operator

Selected threshold values are around 70.Too small values of threshold mean small change of graylevel and too big threshold value may gives noise.

Original image Segmented image Original image Segmented image

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Segmentation by thresholding

• Simple Global Thresholding– Simply select a threshold value between peak

values from a histogram plot and set zero below than threshold and set 255 greater than threshold.

– Disadvantage: lost a lot of data information and there is no guarantee of grouped (well-separated) histogram.

5.8.98 Hyeun-gu Choi

Segmentation by thresholding

• Multiple Thresholding– Select useful region by selecting two thresholds– Simple and very good results– Disadvantage - No guarantee for grouped

histogram. Not good for automated system because user should decide threshold value

5.8.98 Hyeun-gu Choi

Examples of multiple thresholding(2)

Original spindensity image

Enhanced image byhistogram equalization

Segmented image(CSF tissue)70 - 230

* CSF (Cerebrospinal fluid)

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Examples of multiple thresholding

Th = 83~255

Th = 0~82

Noise

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Label Region algorithm

• Basic Concept– Scan through an image pixel by pixel– Compare the gray value of center pixel with

those of top and left pixels.– If compared gray values are the same, those

pixels are categorized to one group.– After scanning all pixels, pixels with the same

gray level value will be grouped.

• Disadvantage - Very vulnerable to noise.

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Label Region algorithm

Result of label regionalgorithm

Modified label regionalgorithm

Modified label region algorithm has some margin. That is, when pixel values are compared, only if pixel value difference is bigger than some value, the algorithm classify the pixel to new class.Modified algorithm is less vulnerable to noise.

Original image

5.8.98 Hyeun-gu Choi

Hough Transform(1)

• Consider a point (xi , yi). General equation for this point is yi = axi + b or b = -xia+yi

• If two points (xi, yi) and (xj, yj) are on the same line two points have an intersection in parameter space (ab plane).

xi,yi

xj,yj

x

y

a

b

b = -xia+yi

b = -xja+yj

a`

b`

xy plane Parameter space

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• Problem - Both slope and intercept approach infinity as the line approaches the vertical.

• Solution - change to plane yi = axi + b x cos + y sin =

Hough Transform

Normal representation of a linex

y

5.8.98 Hyeun-gu Choi

• Subdivision of the parameter space into so- called accumulator cells and counting intersection points.

• Intersection point with a value and a value means two data points are on the same line with and .

Hough Transform

max

max

min

min

Quantization of the plane into cells.

(x1, y1)

(x2, y2)

5.8.98 Hyeun-gu Choi

Hough Transform

Four dots inxy plane

Result of HoughTransform

Four dots in parameter space

Intersections found by accumulation cell

1

1

2

2

3

3

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• Disadvantage of Hough Transform– Vulnerable to noise– Difficult to find lines in complicated images– Difficult to change the shape which looking for

• Line : y = ax + b

• Circle : (x-C1)2 + (y-C2)2 = C3 3Dim parameter space

– Long processing time• About 25 minutes for 192 x 128 size image by

Pentium 166MHz, 48Mbyte RAM

Hough Transform

5.8.98 Hyeun-gu Choi

Hough Transform

Original image Gradient image Found diagonal linesbecause diagonal direction has a lot of data points

Gradient image in parameter space Found intersections

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Hough Transform

Original image Thresholded imageGradient image

threshold

Histogram of original image

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Gradient image in parameter space

Found intersections

Found linesThresholded image

Hough Transform

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Graphic User Interface (Widget)

• A graphic user interface is created for demonstration of segmentation methods in IDL (Interactive Data Language).

• Widget interface is consisted of five image windows and one plot window.

• 11 full down main menus.

• Advantage - Easy to organize many algorithms and easy to modify.

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Graphic User Interface (Widget)

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• Main menus and sub menus– Files

• New - Get an new image.

• Save Image

– Switch - User can switch two windows– Filter

• High pass, low pass, unsharp, laplacian, vertical edges, horizontal edges, sobel, median, and custom.

– Arithmetic - Add and subtract two windows.

Graphic User Interface (Widget)

5.8.98 Hyeun-gu Choi

– Add Noise – Label Region

• Label region (IDL library) and Modified label region

– Histogram• Histogram of source window, Histogram

equalization, one threshold, and two thresholds

– Color table - Emphasize the processed image.

Graphic User Interface (Widget)

5.8.98 Hyeun-gu Choi

– Hough • Manual - User can choose intersections by clicking

in parameter space.

• Auto - Computer do the whole process.

– Zoom - Zoom in an image with several magnification (2x, 3x, 4x).

– Done - Finish the interface.

Graphic User Interface (Widget)

5.8.98 Hyeun-gu Choi

Reference

• (1) Gonzalez, Rafael C. and Woods, Richard E.[1992] “Digital Image Processing” Addison-Wesley Publishing Company

• (2) Lynn M. Fletcher, John B. Barsotti, Joseph P. Hornak [1993] “A Multispectral Analysis of Brain Tissues” Magnetic Resonance in Medicine 29:623-630