Image Segmenting overlapping variable regions (watershed with edge detection issues)
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I want to segment multiple cells out of one image. I have images with the nucleus as markers and a separate cytoplasm image. There are multiple of these in one image. I have so far tried using the watershed method with markers. The nucleus image is the markers mask I have been using. However, this method has been inaccurate, due the stretches of overlay between different objects. I was wondering if there a specific way I can employ the watershed method to account for these type of images or if I should employ a sort of edge detection.
I was also thinking of perhaps going through each boundary and finding the points connected to each boundary when overlaying the markers on top of the original image. I would then try to employ a decider function for points connected to more than one boundary and see if I can decide which boundary those points belong to. It seems that the streaks coming out of the boundaries do follow a general path shape of the original boundary and are at parallel plane with the boundary.
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Image Analyst
2018-8-18
I think it's hard for even a human to decide where one object stops in that mess of filaments, and where the other begins. So I think you're best off having the user do it manually with imfreehand(). I know that's not what you wanted to hear but it could be your fastest route to a solution. You might spend hours manually tracing them vs. weeks or months trying to figure out how to do it automatically. If the image segmentation is your total project, then by all means try to figure it out. However if the biology is your interest and just need to get the cells identified by whatever means, and image analysis is just a means to the end, then just trace them manually and be done with it and get on with the biology.
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