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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?)

What I have tried:

I tried to do feature extraction from an image
Posted
Updated 2-Aug-20 20:18pm

1 solution

I'm guessing one random pixel out of the 9 in a 3x3 cell (otherwise you would just use a different cell size.)

The confidence factor is related to how many 3x3 cells in a lung; and what the training data contains. Can't just test with 3x3. Need to collect stats on various matrix sizes to see if the error rate is "acceptable."

(Same concept that should have been used to improve corona-virus "sampling" for gauging infection "bubbles" instead of just reacting.)
 
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