1. Detection of crystals in Microarray images P.Dilip Rishabh Jain

2. Introduction Microarray grids are used by the chemical researchers who are interested in finding which chemical reactions and under what conditions produce crystals. A Microarray is a collection of chemical droplets in wells arranged as a grid

3. Each microarray image has –  An octagonal boundary  The central part inside the boundary which is highly illuminated. Illumination gradually decreases as we move away.  A droplet inside the boundary.  Crystals inside the droplet (found very rarely).

4. Microarray Cell…

5. Work done so far… Edge detection using Prewitt Applied Hough transform for circles on the edge detected image in order to detect the droplet.

6. Applied Hough transform for straight lines (after converting the image into binary and then applying edge detection) in order to detect the octagonal boundary. Removed the part outside the boundary and kept only the necessary part for further processing.

7. Modelled the background of the image obtained after removing the part outside the boundary. This was done so that we can extract the foreground of the image by filtering out the image with the background model already created.

8. The background of many images are almost symmetric with respect to its vertical or horizontal or diagonal axis passing through its mid point. Used this property of the images to model the background by reflecting half of the image against its corresponding axis.

9. Original Image Inside Boundary Background Foreground

10. Drop detection… In the above foreground image the region in which the drop lies has higher illumination Applied a threshold on the foreground image to get the region with the drop

12. The above method is not the best method of modeling background. Another method we propose to tackle this problem is first equalizing the illumination pattern in the image and then detect the region containing 'interesting points'. This region will contain the droplet and hence the crystals inside (if present).

13. The illumination pattern in the images follow a Gaussian like curve. Illumination pattern in imageGaussian curve

14. Used a scaled version of gaussian to model the illumination pattern. Probed on different values of the scaling factor and standard deviation to get the optimal curve. Subtracted this from the image to equalize the illumination.

15. Illumination equalization… Before Gaussian After

16. Detected the droplet using edge detection (sobel). After the removal of boundaries, the only edges left in most of the images are the edges in the droplet region. We use this property of the binary image obtained to detect the droplet region.

20. Future work… Try to equalize the illumination of the image using a more appropriate function which would be much more complicated than a Gaussian. Extract the features from the region we get and then train it with the help of labeled data.

21. References “Digital Image Processing” by Gonzalez & Woods “Image Analysis of Coccidian Parasite” by Sumedha Gholba, Alisha Rossi and Bismaya Rath http://www.mathworks.com/ http://homepages.inf.ed.ac.uk/rbf/HIPR2/hough.htm http://astronomy.swin.edu.au/~pbourke/other/distrib utions/