Flood fill algorithm - Demo
Fatma Samy
SA,
Flood fill algorithm is an algorithm used to fill an area of certain color with another color.
We use it in our application after extracting the lung borders to refill the lung area with the original intensity of the source image.
Here's a little demo for this algorithm, the image is fixed, and don't spend much time asking your self what this image could represent :D
it's th image resulted from sobel edge detection of lung CT image, anyway :D
all you have to do is to choose a color and click the area you want to fill, and get the image colored ;)
It's a direct implementation of the third pseudo code that could be found here
http://www.codecodex.com/wiki/Implementing_the_flood_fill_algorithm


My implementation is downloadable from here
 any comments or questions are appreciated :)
Thanks :)
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Insight Segmentation and Registration Toolkit (ITK)
Fatma Samy
SA,
ITK is an open source system that provide developers lots of ready to use tools for image processing especially medical images segmentation and registration.
It's really useful I advise whoever going to deal with medical images to use this toolkit it's helpful (Y).


But since it's implemented in C++ and we use C# in our project, I used a wrapper called ManagedITK which covers the majority of ITK functions.


For more info please refer to:
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http://www.itk.org/ ---> The ITK project


http://code.google.com/p/manageditk/ ---> The ManagedITK
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Redefine our goal
Fatma Samy
SA,
It has been while I know :)
But here we're again ;)


I don't know if the title is expressive enough !! But it's ok :D


I just felt that we need to redefine the goal of the project and the output we aim to gain. May be this is because  we read more and asked more, so actually I feel that we keep discovering what we really have to do. But I promise we won't keep discovering till the Final seminar isA (A) :D


Well let me sum up what we aim to :
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- CBIR based CAD system for lung tumors.
- The system should be totally automated so that the user (physician) will not have to do much work in order to make the system able to identify the tumors.
- The automation is achieved through:
   - Automatic lung segmentation
   - Automatic identification of tumor candidates.
   - Automatic feature extraction.
   - Evaluation of the tumor characteristics which gives an initial diagnosis of the   tumor (Malignant or Benign).
   - By retrieving similar tumors we can confirm our initial diagnosis.
   - Finally we show the physician the previous cases and probability that this tumor can be malignant or benign.
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