Confusion matrix to validate image segmentation
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I have a segmented image of an xray image. I need to make a confusion matrix to validate that segmentation. Can anyone tell me please how can I apply a confusion matrix to validate image segmentation?
The origial image and te segmented image are shown below.
The first one is the original image and the second one is the segmented image.
![152.bmp](https://www.mathworks.com/matlabcentral/answers/uploaded_files/144702/152.bmp)
![skullOnly2.bmp](https://www.mathworks.com/matlabcentral/answers/uploaded_files/144703/skullOnly2.bmp)
3 个评论
Image Analyst
2018-12-23
Plus (you probably already know this) you can't make a confusion matrix on just one image. You'd need at least two and preferably hundreds or thousands.
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Image Analyst
2018-12-23
If you have the Statistics and Machine Learning Toolbox, you can use confusionmat() and confusionchart(). Of course you still have to do a bunch of experiments to determine true positive, true negatives, false positives, and false negatives. I assume you know how to do that. It's basically a list of whether you got the diagnosis right or wrong. You have to decide how to determine if the segmentation is right or wrong, like use the Sorensen-Dice metric to see how much of your segmentation overlaps a hand drawn segmentation by the radiologist.
5 个评论
Image Analyst
2018-12-27
What parameters do you have available for you to vary? The threshold and minimum blob size for example? So you pick one set of parameters and determine if the image is a true positive, true negative, false positive, or false negative. Do it on a bunch of images and determine the counts for those, so the sum of those 4 results will sum to 200 if you had 200 images. Then you vary the parameters and try again. Each time you vary the parameters, you will get a point on the ROC cuve and two values you can add to your confusion matrix.
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