Week 3

Due to July 4th, this week has been shorter. I spent all the time working on my project: Segmentation of 2 photon zebra fish neuroimages.

As I have mentioned in my last blogger, I implemented some simple algorithms last week. However, they don't work very well. Specifically, classic watershed methods is very sensitive to noise, resulting in over segmentation in the background area. This week, I found another paper about watershed-based neutrosophic approach to solve the problem of over segmentation.

Then, I am going to explain this algorithm, including how it works and the results of each step.

Overview:
Watershed image segmentation is good for handling uniformed background and objects with blurry edges. In this paper, objects are T and background is F. The blurry edges are gradually changed from objects to background. The blurred boundaries are defined in I.

Step1: Map image and define {T, F}
  1. A 3 x 3 mean filter is applied;
  2. The the image is converted by using S-function.
    Results:
    Fig 1a: T; 
Fig 1b: F.
Step2: Enhancement using intensification transform to enhance the image in the new domain.
          Results:
Fig2: T after enhancement.
Step3: Find the threshold in T and F.
          A heuristic approach is used to find the threshold in T and F:

          Results:
Fig 3a: Image of applying threshold to T. 
Fig 3b: Image of applying threshold to F. 

Step4: Define homogeneity in intensity domain and decide {I}.
           Homogeneity H is defined using standard deviation and discontinuity of the intensity.
           Results:
Fig 4a: Standard deviation. 

Fig 4b: Discontinuity. 

Fig 4c: Homogeneity. 

Fig 4d: I. 
Step5: Convert the image to a binary image based on {T, I, F}

Step6: Applying watershed to the converted binary image:
           Results:
Fig 5: Watershed segmentation of binary image. 








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