How to extract highest intensity area from a greyscale spectrogram?

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Hello.
I got greyscale mel-spectrograms images from a dataset and I want to divide it into several areas and to obtain the area where pixels have highest intensity in average.
This will be useful to label the images with rectangles for a deep learning training.
Hope somebody will help me.
I attach a greyscale spectrogram I got where you can show me the way to do it.
Thank you.

Accepted Answer

Image Analyst
Image Analyst on 23 Jan 2021
You've given no criteria for how those areas are to be determined. You might want to use watershed() or superpixels(). Or use imbinarize() to segment on intensity (adaptive or global), or multithresh() for several global thresholds.
  3 Comments
Image Analyst
Image Analyst on 23 Jan 2021
Oh, OK. I would have used blockproc() if you wanted rectangular blocks but glad you solved it somehow.
I'm attaching several blockproc() demos in case you're still interested.

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More Answers (1)

KALYAN ACHARJYA
KALYAN ACHARJYA on 22 Jan 2021
Edited: KALYAN ACHARJYA on 22 Jan 2021
  1. Apply thresholding to cluster the image into two segment, certain higher pixel and lower value pixels.
  2. Get the largest blob as per requirement. (bwareafilt function)
What does "Major Intensity" mean here?
  5 Comments
KALYAN ACHARJYA
KALYAN ACHARJYA on 23 Jan 2021
Edited: KALYAN ACHARJYA on 23 Jan 2021
"but this code you wrote here shows a black image".
Most probably, there is only one maximum value pixel, so it is not easily visualized (check carefully). You can confirm the same with the extract_roi matrix, which must be the non-zero matrix.
Claudio Eutizi
Claudio Eutizi on 23 Jan 2021
Yes I found that pixel you're talking about.
I managed to do what I asked to in this question.
Can you help me with this? Thank you so much.

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