Computer Vision REU Week 8 & 9
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Computer Vision REU Week 8 & 9. Adam Kavanaugh. Set to be matched. Went to Dr. Sugaya and confirmed an “ideal” match. Same Gene, different brain. The problem. Two main areas of concentration The matching problem
Computer Vision REU Week 8 & 9
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Computer Vision REUWeek 8 & 9 Adam Kavanaugh
Set to be matched • Went to Dr. Sugaya and confirmed an “ideal” match. • Same Gene, different brain
The problem • Two main areas of concentration • The matching problem • Match an input gene expression to a gene or set of genes from a defined database. • Automatic data processing • Given large unprocessed slides with brain slices, segment the brains and prep them for analysis
Data Processing • Basic Procedure: • Remove the background • Cut out each individual slice • Normalize the slices • Rotate the slices so they line up • Apply a threshold to remove any brain material which does not express the given gene
Segmentation • Uses connected components with pixel ranges for the components based on the background average • Flag the background to white, and rerun connected components except only allowing 2 pixel ranges: 0 – 254 and 255 • Cut out each individual component that meets a threshold value
Connected Components • Used a very basic connected components method where it only checks its left and top neighbors • Implemented the merge function by rescanning the image and swapping component values • This later caused some problems
Size Problem • Due to my naïve implementation of the merge function in the connected components, the program is VERY inefficient • A Test run on the large image (10768 x 4072) never completed. • Ran overnight for 7 hours and was only ¼ through the image and slowing
Solution • Run the image through Gaussian Pyramid and pass the smallest level to the segmentation program. • This method completed in about 1 minute as opposed to the estimated 30+ hours of the other. • However, this causes a loss of detail which is important down the line
Saving the details • Use the knowledge that the Gaussian Pyramid cuts the image by a constant factor • Make bounding boxes around each brain component in the smaller image • Multiply the corner coordinates of each box by 8 to get the corresponding positions in the full image. • Rerun the first stage segmentation on the new slices which runs much faster
Results - Individual Original Final
After segmentation • Throw out any bad slices based on certain thresholds • Then apply the rest of the processing • Normalize • Rotate • Threshold
Ideal Result • In the end, the results should look something like this • This slice was manually segmented, however the rest of the process was applied.
Future Improvements • Works best on “well behaved” slides while other slides throw off the components • Try to improve this situation to make it more viable for all slide variations • Work out the bugs with cutting the segmented slices out • Integrate the other parts of the processing
Matching Problem • Current Methodology: • Pulled primitive data from Guo-Hall skeletons and color histograms • Performed Sum of Squared Differences method to get a score and created a database • Tested various input slices for accuracy.
Results • Questionable for the skeleton data. • Matches are not consistent across genes • Promising results for the color histogram matching. • For the top 5 scorers, there are at least 3 brains of the same gene type
Additions to be made • Need larger scale matching • To much variation with edges and interiors to use specific information like canny edges • Applying a weighted scoring and integrate all of the matching data sets. • Pull more data to match off of.
