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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
5.8.98 Hyeun-gu Choi
• 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
5.8.98 Hyeun-gu Choi
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)
5.8.98 Hyeun-gu Choi
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.
5.8.98 Hyeun-gu Choi
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
5.8.98 Hyeun-gu Choi
• 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
5.8.98 Hyeun-gu Choi
• 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
5.8.98 Hyeun-gu Choi
Hough Transform
Original image Thresholded imageGradient image
threshold
Histogram of original image
5.8.98 Hyeun-gu Choi
Gradient image in parameter space
Found intersections
Found linesThresholded image
Hough Transform
5.8.98 Hyeun-gu Choi
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.
5.8.98 Hyeun-gu Choi
• 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)