Brain tumor detection in CT data

6 views (last 30 days)
Hello all!
I'm looking for 2d matlab implementation of random tumor detection algorithm in computed tomography images. And then should be performed a quantitative assessment of the proposed algorithm, based on the relative number of correct detections, false and invalid such discoveries.
I really need this help.
Big thanks in advance.
Regards!

Accepted Answer

Matt Kindig
Matt Kindig on 23 Jul 2012
Hi Peter,
Nobody is going to be able to give you a full turnkey solution to your problem, largely because a) such a task is pretty substantial and outside the scope of this forum, and b) none of us have your CT images, nor do we know what type of criteria you want to use to categorize the tumors. Do you have the Image Processing Toolbox? If so, you should start by reviewing the demos there-- several of them are relevant to image segmentation and should get you started. Pay particular attention to the use of the regionprops() function.
After you have done that, you can return here with _specific _questions. It would also be helpful if you post some sample images here to illustrate what features you are looking for.

More Answers (1)

Walter Roberson
Walter Roberson on 23 Jul 2012
Edited: Walter Roberson on 23 Jul 2012
Is it the tumor locations that are random, or is it that the detection algorithm should use randomness to pick the "locations" it will say are tumors, or is it that you have a number of detection algorithms but on any one run, some particular algorithm out of the set should be chosen randomly ?
  3 Comments
Peter Petrov
Peter Petrov on 24 Jul 2012
Edited: Peter Petrov on 24 Jul 2012
Is the following block diagram good for CT images?
MRI Scan -> [Preprocessing] -> Grayscale Image -> High pass filter -> Enhanced image -> [Post Processing] -> Threshold Segmentation -> Watershed Segmentation -> Morphological Operators (eroding, dilating) -> Output image with tumor
Walter Roberson
Walter Roberson on 24 Jul 2012
If the detection algorithm proposes a tumor position that is not exactly the same shape and position as the "known" tumor, then what measure should be used to decide how "good" the detection was?
What are the relative penalties for detecting as tumorous a pixel that is not part of the set, compared to the penalties for detecting as non-tumorous a pixel that is part of the set? Or do the penalties vary positionally? e.g., low penalty for giving an extra boundary pixel but high penalty for picking out as tumorous a pixel that is distant from any of the tumors?
What penalty for detecting an area as being two distinct tumors that touch and together cover the area that is really only one tumor?

Sign in to comment.

Categories

Find more on Biomedical Imaging in Help Center and File Exchange

Products

Community Treasure Hunt

Find the treasures in MATLAB Central and discover how the community can help you!

Start Hunting!