MATLAB: Image segmentation using two binary masks

cbirconnected componentImage Processing Toolboximage segmentationmorphological reconstructionregion growing

The aim of my image segmentation task is to completely extract the object of interest. I have two image segmentation methods. With the first method I get a portion of the desired object (not all of it, somewhere around 10-50% of the total object) but there are no false positives in this image segmentation mask.

With the second method I get all of my desired object along with other false positives. Now I need to use these two images to completely extract my object of interest. I was thinking along the lines of using connected component analysis or using the first image as a seed to grow the region in the second image. Any thoughts or suggestions would be welcome (pertinent to the topic, of course).

Below I've attached the two images i am referring to. the bottom one is the minimal mask and the top one is the noisy mask.

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It would be useful to see the original image. But if you're sure you can get the large blob, knowing that the small blob is contained within it, then you can use imreconstruct(). It will take one of more "markers" in a binary image. Then, given a binary image which contains more blobs, some of which may contain the small marker blobs, and some blobs which don't contain any marker blob, then it will give you only the blobs that contain marker blobs and exclude the blobs that don't contain marker blobs.

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MATLAB: How to remove the background from a binarized image of which I selected a specific ROI

I'm confused. What is the "main matrix" part of the image? And what are pores and what is background? The images look the same, just cropped. I don't see any inverting nor do I see why it's needed.

I'll assume you want the white blobs but not the white circular surround. So simply use a circular template mask to get rid of it:

binaryImage(circularMask) = false;

or call imclearborder() to get rid of the circular surround

binaryImage = imclearborder(binaryImage);

imclearborder will also remove and partial blobs crossing the circular border, which is not a bad thing - just depends if you want area fraction and partial blobs are okay, or if you need complete blobs for your computations.

Then call imrect() or rbbox() to define the cropping rectangle, call imcrop() or use indexing to actually make the crop, and you're done.

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Shouldn't be too hard unless the noise is a problem. I'd do these steps:

Reduce noise - there are a variety of methods.
Threshold at a high level to find bright things.
Use bwareaopen to get rid of small objects.
Call bwlabel and regionprops and use perimeter^2/(4*pi*area) to find round objects.
Use ismember() to keep the round objects only. Now you have just the white circle part.
Threshold at a medium level to get medium bright things.
Use watershed if necessary to split apart touching blobs.
Use imreconstruct to get only the gray part that contains the white circle.

Here are some examples to help you:

http://www.mathworks.com/matlabcentral/fileexchange/25157-image-segmentation-tutorial-blobsdemo

http://blogs.mathworks.com/steve/2006/06/02/cell-segmentation/

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