Methods for analysis of plots in image processing

Hello everyone.

I have difficulty in image processing. I have to figure out a specific length through a plot that counts each pixel value of the image. I try to obtain the length through the x-values of the first y=0, and the x-values of the second y=0. Also, I need to analyze hundreds of images, so I need to repeat code accordingly. I want to save the final x value as an Excel file.

I would like to hear the opinions of various experts.

Thank you.

[KSSV]

Attach single image.....

[JCH]

Thank you for your reply :)

Answer 1

[Constantino Carlos Reyes-Aldasoro]

This seems rather simple. First, project your data to a single dimension, say your image is called data_2D,

data_1D = sum(data_2D,1);   % or change the 1 to 2 depending on the orientation of the image

This will sum all the elements of each column/row. Then you want to avoid the noise so threshold above a certain level, say 5 and use find to locate first and last points

find(data_1D>5,1,'first')
find(data_1D>5,1,'last')

Then you can insert that in between a loop that will process all your data sets.

Hope this solves your problem. If it does, please accept the answer, if it does not, let me know.

[JCH]

Thank you so much friend. Your reply helped me a lot.

Answer 2

[Image Analyst]

Another way. If you want to look at only the largest blob, and ignore the disconnected noise, call bwareafilt() first, then call regionprops()

mask = bwareafilt(mask, 1); % Take largest
props = regionprops(mask, 'BoundingBox'); % Find bounding box location.
boundingBox = props.BoundingBox
topRow = ceil(boundingBox(2))
bottomRow = ceil(boundingBox(2) + boundingBox(4))

Or you can sum the mask sideways and threshold to find rows with a significant number of white pixels:

verticalProfile = sum(mask, 2)
inSpray = verticalProfile > 10; % Only find rows with more than 10 white pixels.
topRow = find(inSpray, 1, 'first');
bottomRow = find(inSpray, 1, 'last');

[JCH]

Thank you. I get more professional knowledge.