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MCS Project - Enhanced Watershed

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This is presentation of my MCS degree project 'Enhanced Watershed'.

This is presentation of my MCS degree project 'Enhanced Watershed'.

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  • 1. Enhanced Watershed Image Processing Segmentation Aamir Shahzad CIIT/SP05-MCS-002/WAH
  • 2. Abstract
    • Watershed enhancement can be done in three different ways.
    • One is to perform preprocessing. Second is to improve the watershed algorithm. And the third one is to perform post processing.
    • I choose the last option for enhancement because this option has more opportunities for enhancement. In this option, I use the technique “Fusion of edge based enhanced watershed segmentation” or “edge based enhanced watershed segmentation”.
  • 3. What is Watershed Segmentation?
    • What is segmentation?
    • Understanding the watershed transform requires that you think of an image as a surface.
    • The key behind watershed is to change your image into another image whose catchment basins are the objects you want to identify.
  • 4. Marker-Controlled Watershed
    • In simple watershed, we face the problem of over segmentation.
    • Marker control is a improved form of watershed.
    • Here we use internal and external markers define by automatically or from user
  • 5. Proposed system – enhanced watershed segmentation
    • Following are the high level main steps
      • Perform watershed segmentation on main image
      • Get edge image from main image
      • Enhance edge image results
      • Merge the results based on the best results
  • 6. Algorithms used in main algorithm
    • Connect edge with border
    • Fill one missing pixel
    • Get big object number
    • Connect object with border
    • Get minimum object size
    • Get object start pixel
    • Get object number
    • Get object size
    • Get select object
  • 7. Proposed algorithm
    • Read image
    • Convert image to gray scale, if required
    • Perform canny edge detection and get edges
    • connect edges with border
    • Fill missing pixel in edges
    • make edges logical (i.e. 0/1)
    • Complement the image
    • Perform labeling function on edges and get label 1 and total objects in label 1
    • Get biggest object number in the label 1
    • Connect objects with border
    • Perform labeling function again and get label 1 and total objects in label 1
    • Get biggest object number again in the label 1
    • Perform existing watershed method and get the label 2
    • Perform labeling function on label 2 and get total objects in label 2
    • Get biggest object number in the label 2
    • Get the size of minimum object in label 2
    • loop through 1st object to total object in label 1
      • if current object number is equal to biggest number in label 1 then continue
      • Get the current object’s start pixel in x and y variable from label 1
      • Get the object number at x and y position in label 2
      • if object number is equal to the biggest object number of label 2 then
        • Increase the value of total objects in label 2 by one
        • Find the rows and columns pixels of current object in label 1
        • Find the total pixels (i.e. total rows or columns) in above find rows and columns
  • 8.
        • Loop through 1st pixel to the last pixel of current object in label 1
          • Change the current pixel value at label 2 to total objects value in label 2
          • if there is other objects pixel in between rows then change the pixel to total objects value
        • continue the loop at 17
      • Get the current object size from label 1
      • Get the size of object number (see 17.c)
      • If current object’s size is greater than object number’s size then
        • Find the rows and columns pixels of current object in label 1
        • Find the total pixels (i.e. total rows or columns) in above find rows and columns
        • Loop through 1st pixel to the last pixel of current object in label 1
          • Change the current pixel value at label 2 to object number value (see 17.c)
        • continue the loop at 17
      • If current object’s size is greater than double size of minimum object in label 2
        • Find the rows and columns pixels of current object in label 1
        • Find the total pixels (i.e. total rows or columns) in above find rows and columns
        • Increase the value of total objects in label 2 by one
        • Loop through 1st pixel to the last pixel of current object in label 1
          • Change the current pixel value at label 2 to total object value
          • if there is other objects pixel in between rows then change the pixel to total objects value
        • continue the loop at 17
    • Convert the label2 to RGB and display the final enhanced watershed result
  • 9. Example 1 Original Image Simple Watershed Result Marker-Controlled Watershed Result My Watershed Result
  • 10. Example 2 Original Image Marker-Controlled Watershed Result My Watershed Result
  • 11. Example 3 Original Image Marker-Controlled Watershed Result My Watershed Result
  • 12. Example 4 Original Image Marker-Controlled Watershed Result My Watershed Result
  • 13. Note: Results approximate 1% 20% 20% 0% 6 1% 30% 30% 0% 5 1% 70% 70% 0% 4 1% 10% 50% 40% 3 3% 20% 70% 50% 2 My watershed Watershed 1% 10% 80% 70% 1 Fault Enhancement Image No
  • 14. 2% 30% 30% 0% 12 1% 705 70% 0% 11 1% 90% 90% 0% 10 2% 25% 30% 5% 9 1% 3% 53% 50% 8 My watershed Watershed 1% 70% 70% 0% 7 Fault Enhancement Image No
  • 15.  << The End >>  2% 36% 54% 18% Average 10% 0% 25% 30% 15 1% 90% 90% 0 % 14 My watershed Watershed 1% 5% 35% 30% 13 Fault Enhancement Image No