Image segmentation by using thresholding

1. Under the guidance of:
Dr. Subrajeet. Mohapatra
Presented by
Vani A. Hiremani
PhD|CSE|10003|2018
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2.  Image Segmentation
 Image Segmentation classification
 Thresholding
 Automatic thresholding algorithm
 Example of Thresholding
 Conclusion
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3.  Image Segmentation is a procedure that describes the process of
dividing an image into non overlapping, connected image areas,
called regions, on the basis of criteria governing similarity and
homogeneity.
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Segmented

4.  Discontinuity based
o Detection of Isolated Points
o Detection of Lines
o Edge Detection
 Similarity based
o Thresholding
o Region growing
o Region Splitting and Merging
o Clustering
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5.  Thresholding is a technique of segmenting the a binary image based
upon a threshold value.
 Image thresholding is very useful for object extraction and
background rejection.
 Belongingness of each pixel to object or background is decided on the
basis of a particular threshold.
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6.  Image histogram describes the frequency of the intensity values that
occur in an image. Histogram can be very efficiently used for
determining the threshold for image segmentation.
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7.  Ideal bimodal histogram consists of peaks corresponding to the object and
background regions and a valley in between.
 The object and background of images with bimodal histogram form two
different groups with distinct gray levels.
 Bi–level thresholding is employed for such images. So a threshold T has to be
selected from the valley region for segmenting the image.
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Peak 1
background
Peak 2
object
<T<

8.  A single threshold is enough for segmenting an image with
bimodal histogram and is called bi–level thresholding.
 For an image f ( x , y ) with an bright object and dark background,
the binary segmented image can be mathematically represented as
 g ( x ,y )= 1 if f ( x ,y ) ≥ T ⇒Object
0 if f ( x ,y ) < T ⇒ Background
 Every pixel intensity value has to be compared with the threshold
T to classify each pixel as a background or an object pixel.
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9.  Selection of proper threshold is essential for every threshold based
segmentation technique. This threshold value of the thresholding
operation can be considered as an operation that invokes testing
against a function T where this function T is of the form
T = T[(x, y), p(x, y), f(x, y)]
 where, (x, y) ⇒Pixel Location p(x, y) ⇒Local property in a
neighbourhood cantered at ( x , y ). f(x, y) ⇒ Pixel intensity at ( x , y
).
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10.  So in general this threshold T can be a function of pixel Location,
local property within the neighbourhood and pixel intensity value.
 Threshold T can be a function of any combination of the above three
terms. Depending on this combination the threshold T can be
classified as
◦ Global Threshold
◦ Local Threshold
◦ Adaptive Threshold
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11.  If the threshold T is only a function of pixel intensity value f ( x ,y ).
Then T is termed as global threshold.
T [f(x,y)] ⇒ Global Threshold
 Threshold T is termed as local threshold if T is a function of pixel
intensity value and local property.
T[f(x,y),p(x,y)] ⇒ Local Threshold
 If the threshold is a function of all the three properties then T is
termed as adaptive threshold.
T[(x,y),f(x,y),p(x,y)] ⇒ Adaptive Threshold
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12.  Using this threshold T we want to get a Thresholded binary image g (
x , y ) defined as
 g ( x ,y )= 1 if f ( x ,y ) ≥ T ⇒ Object
0 if f ( x ,y ) < T ⇒ Background
 This threshold T can be global, local or adaptive.
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13. Step 1: Select an initial value of threshold T .
Step 2: Use T to segment the image into two groups G 1& G 2
Step 3: Compute the mean µ1 and µ2 for each group of pixels.
Step 4: Compute the new updated threshold T using the relation
T = µ1 + µ2
Step 5: Repeat step 2-4 until the mean values µ1 and µ2 in successive
iterations do not change.
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14.  Image segmentation is an essential preliminary step in image analysis
and interpretation.
 There is no universal algorithm or segmentation technique for all
kind of images.
 Specific methods have to be developed for segmenting particular
kind of images.
 None of the segmentation evaluation measure are perfect.
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