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How to do feature extraction from an image?

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sara on 2 Aug 2020
Commented: sara on 31 Aug 2020
Hi I want to do feature extraction from an image. I read a paper and did this steps: I did image segmentation. Then I want to do feature extraction. In this paper:
Segmented lungs were divided into 3*3 windows in which all nine pixels were located in the lung mask. Window size selection is a compromise between higher resolution (in the classification process) and faster algorithm. Smaller windows (i.e. 1*1 or 2*2) have the problem of more time complexity for training and increaseing the number of FP. Larger windows (i.e. 5* 5 or larger) cause lower resolution of reconstructed image after classification and miss some tiny nodules. Thus, for better resolution and faster algorithm, simultaneously, we used a 3*3 window. In the training process, these windows were labeled as nodule (þ1) and non-nodule (1).
My question is this: Is there any standard criteria to lable the 3*3 window as a noudle? ( I mean if how many of these pixcles are 1, we should lable the window as a noudle?)</pre>
Image Analyst
Image Analyst on 4 Aug 2020
I don't think they segmented the image. I think they did that on the original gray scale image. I don't think it would make any sense to do a covariance of 9 pixels if the 9 pixels were segmented, which means they are already binary/logical.
sara on 31 Aug 2020
thanks dear Image Analyst. I think I made a mistake. I read the paper again. I knew they segment region of interested from background and then they do this operation on gray scale image.

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