Md. Habibur Rahman
American International University Bangladesh
Presented By
Md. Habibur Rahman* and Md. Rafiqul Islam
Aut...
Main idea of our paper
• The main idea is to propose a modified version
of the watershed algorithm for image
segmentation
...
Overview of Image Segmentation
 Image Segmentation is the method of
assigning a label to each pixel in an image
 The goa...
Overview of Image Segmentation (cont.)
 Hill Climbing with K-Means (HKM)
 This method detects local maxima of clusters i...
Modified Watershed Algorithm
 It can quickly calculate
the every region of the
watershed segmentation
 Image normalizati...
Modified Watershed Algorithm (cont.)
 To determine the adaptive threshold by Eq. 2 and Eq.
3 based on Gray-threshold func...
Modified Watershed Algorithm (cont.)
 Impose Minima to create morphological process image
using Nucleus-masking (M2) on t...
Modified Watershed Algorithm (cont.)
 Convert three channels into a RGB image for
visualizing the labeled regions by Pn =...
Overview of evaluation metrics
 Peak Signal to Noise Ratio (PSNR) is calculated
between two images by Eq. 6.
 Mean Squar...
Overview of evaluation metrics (cont.)
 The image quality metrics like PSNR of each YUV color
channel (Y, U and V) is cal...
Result Analysis
11
12
13
Conclusion
 Our proposed MWS method ensures accuracy and
quality of the 10 different kinds of color images
 Proposed mod...
Questions ?
Thank You
for
Kind Attention
15
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Segmentation of Color Image using Adaptive Thresholding and Masking with Watershed Algorithm

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Segmentation of Color Image using Adaptive Thresholding and Masking with Watershed Algorithm

  1. 1. Md. Habibur Rahman American International University Bangladesh Presented By Md. Habibur Rahman* and Md. Rafiqul Islam Authors ICIEV 13, 17-18 May, 2013, Dhaka, Bangladesh
  2. 2. Main idea of our paper • The main idea is to propose a modified version of the watershed algorithm for image segmentation • An adaptive masking and a thresholding mechanism over each color channel before combining the segmentation from each channel into the final one. • We have compared it with FCM, RG and HKM with respect to PSNR , MSE, PSNRRGB and CQM in 10 different kinds of images. 2
  3. 3. Overview of Image Segmentation  Image Segmentation is the method of assigning a label to each pixel in an image  The goal of image segmentation is to cluster of pixels in the relevant regions  Fuzzy C-Means (FCM)  Partition a finite collection of pixels into a collection of "C" fuzzy clusters  Region Growing (RG)  Group of pixels with similar properties to form a region 3
  4. 4. Overview of Image Segmentation (cont.)  Hill Climbing with K-Means (HKM)  This method detects local maxima of clusters in the global three-dimensional color histogram of an image  It associates the pixels of an image with the detected local maxima  Watershed (WS)  This method comes from geography  It is that of a topographic relief which is flooded by water  Watershed lines being the divide lines of the domains of attraction of rain falling over the region 4
  5. 5. Modified Watershed Algorithm  It can quickly calculate the every region of the watershed segmentation  Image normalization has been done by Eq. 1 5
  6. 6. Modified Watershed Algorithm (cont.)  To determine the adaptive threshold by Eq. 2 and Eq. 3 based on Gray-threshold function  N-dimensional convolution for smoothing image  Adaptive masking operations by Eq. 4 and Eq. 5 6
  7. 7. Modified Watershed Algorithm (cont.)  Impose Minima to create morphological process image using Nucleus-masking (M2) on three color channels  Apply Watershed algorithm (Wn) on three color channels  Pixel labeling calculated by Ln = BWLABEL (Wn) 7
  8. 8. Modified Watershed Algorithm (cont.)  Convert three channels into a RGB image for visualizing the labeled regions by Pn = label2rgb (Ln)  R, G and B color channels (Pn) are added to generate segmented image 8
  9. 9. Overview of evaluation metrics  Peak Signal to Noise Ratio (PSNR) is calculated between two images by Eq. 6.  Mean Square Error (MSE) is calculated pixel-by-pixel by adding up the squared difference of all the pixels and dividing by the total pixel count using the Eq. 7.  Image Quality Measure (CQM) based on color transformation from RGB to YUV.  Reversible YUV Color Transformation (RCT) that is created from the JPEG2000 standard in Eq. 8 9
  10. 10. Overview of evaluation metrics (cont.)  The image quality metrics like PSNR of each YUV color channel (Y, U and V) is calculated separately  Finally, CQM value is calculated using the Eq. 9  Where, weighted luminance quality measure and weighted color quality measure components  Cw and Rw means the weights on the human perception of these cone and rod sensors  Cw and Rw are 0.0551 and 0.9449 respectively 10
  11. 11. Result Analysis 11
  12. 12. 12
  13. 13. 13
  14. 14. Conclusion  Our proposed MWS method ensures accuracy and quality of the 10 different kinds of color images  Proposed modified watershed approach can enhance the image segmentation performance  It is worth noticing that our proposed MWS approach is faster than many other segmentation algorithms, which makes it appropriate for real-time application  According to the visual and quantitative verification, the proposed algorithm is performing better than three other algorithms.  In future, we will focus on a more standard performance measure which could well reflect the difference between segmentation results 14
  15. 15. Questions ? Thank You for Kind Attention 15
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