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Computer Vision REU Week 12

Computer Vision REU Week 12. Adam Kavanaugh. Recap from Last Time. Began work with a blurring method to improve mask generation and background removal accuracy Found that only 30/71 slides were having brain slices cut from them with previous “Slicer” implementation

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Computer Vision REU Week 12

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  1. Computer Vision REUWeek 12 Adam Kavanaugh

  2. Recap from Last Time • Began work with a blurring method to improve mask generation and background removal accuracy • Found that only 30/71 slides were having brain slices cut from them with previous “Slicer” implementation • As of Friday, “Phase 2” is on hold pending Dr. Lobo’s approval.

  3. Blurring the Database • In an attempt to improve the ability of the slicer to extract brains from the slides, I applied a blur to the D3 level of all the slides. • Blur was applied at Sigma values of 1, 2 and 3 to find which of the 3 values was best suited for this purpose.

  4. Sigma 3: Analysis • At Sigma 3, slice cuts improved dramatically on the very noisy slides • However, most cuts were not usable as they merged together multiple brains into a single mask. • Extracted slices from 70/71 slides.

  5. Sigma 2: Analysis • At Sigma 2, merging of multiple brains becomes less of a factor, but this trades off with the loss some slides all together as well as a loss of detail around edges. • However, this resulted in more “useable” brain slices than at the Sigma 3 level. • Extracted slices from 68/71 slides

  6. Sigma 1: Analysis • Best overall sigma value for the D3 image level. • Least amount of combined brain slices, while still cleaning up the background enough to make removal very smooth. • More defined masks than Sigma 2 level • Extracted slices from 70/71 slides, with the highest overall slice count from most slides

  7. Original D3

  8. Without Blur

  9. Sigma 3 Example

  10. Sigma 2 Example

  11. Sigma 1 Example

  12. Overall Blur Effect on Input • Half of the data slides had a moderate to high level of noise and other background problems. • Applying a blur to the D3 level smoothed out much of this noise. • Sigma 1 worked best due to the decreased detail at the D3 level which caused many brains to be merged

  13. From Friday.. • Devastation Struck! • Other efforts put on hold. • Worked with Dan over the weekend and on Monday to resolve the issue with the matcher. • Current status: Stable Condition, Recovered.

  14. Some Matching Tests • In support of Dan’s efforts I got his full system and modified it for my testing purposes. • At the time he was testing different threshold levels starting from 33% and moving Up • I began at the top with 100% and also tested at 90%

  15. Full Pixel Allowance • Matcher results on test query of slide MnSOD: • Top 6 slide matches: • 1) MnSOD [1.084780] • 2) MnSOD [1.127043] • 3) MnSOD [1.138518] • 4) MnSOD [1.138938] • 5) BAPP-LPSLesion-6-2 [1.234906] • 6) MnSOD [1.388898]

  16. 90% Pixel Allowance • Same test using a slice from MnSOD: • Top 6 results: • 1) MnSOD [1.580257] • 2) MnSOD [1.968792] • 3) MnSOD [1.981233] • 4) MnSOD [2.011915] • 5) MnSOD [2.064471] • 6) MnSOD [2.094589]* • *MnSOD matches continue through top 9

  17. Matcher Results and More • As of today, matcher seems to be back up to quality and providing the same, if not better results than we demonstrated previously • Look for a more detailed and in-depth analysis of these and more matcher results on Friday when Dan presents his findings.

  18. The Fate of Blur • Currently, the mask growing technique that Dan has developed works well enough for our purposes in cutting out brain slices. • If another matching criteria is needed, then using the blur to produce the masks might become important due to more detailed masks.

  19. Moving Forward • Continue making adjustments to the 2D system to help bring it closer to finalization. • Familiarize myself with the 3D system so as to begin work sorting out the problem presented to us in Phase 2: “Brain Slicer 2: This Time With Depth!”

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