1. Small Intestine Villi Cell Counting Meghan Olson & Jittapat Bunnag

2. Background/Motivation  Research on Anemia (body can’t sufficiently distribute oxygen=> lack of iron)  Count cells that absorb iron (blue) Currently counted manuallyCurrently counted manually  Time consuming  Inaccurate (different people count differently)  Automated program would be ideal Standardized counting procedureStandardized counting procedure Less time consuming to researcherLess time consuming to researcher

3. Goal  Count number of iron-absorbed (blue) cells  Measure blue and red area, and calculate percentage

4. Complexity  Cell size and color vary  Count Cells only – not light transparent parts  Inconsistency in image Brightness Contrast Color cast Sharpness  Images are unique  Hard to find algorithm that is compatible with all images

5. Algorithm  Preprocessing Separate RGB channels set thresholdsSeparate RGB channels set thresholds  Eliminate background and Villi edge  Separate Red and Blue cells  Count Blue Cells Count # of connected regionCount # of connected region  Calculate Percentage Calculate area of Blue and Red cellsCalculate area of Blue and Red cells

6. Preprocessing  Transform (stretch) image Better contrastBetter contrast  Can separate blue and red cells from image using Green channel Problem: edgeProblem: edge  Eliminate background & edge ~White=> R=B=G~White=> R=B=G Dilate & Subtract from original imageDilate & Subtract from original image EDGE Stretched Background extraction

7. Preprocessing  Red Extraction Create binary templatesCreate binary templates  Red>Blue  Red>Green Combine binary templatesCombine binary templates “And” w/ No-background image“And” w/ No-background image Blue cells Red cells  Blue Extraction Create binary templatesCreate binary templates  Red<Blue  Red<Green Combine binary templatesCombine binary templates “And” w/ No-background image“And” w/ No-background image

8. Works on Variety of images

9. Counting  Convert the processed images into binary images  Eliminate small regions and close small gaps that humans cannot distinguish  Use built-in MATLAB functions to count the area, and the number of cells Area = 9246 Cells = 110 Area = 175018 Cells = 390

10. Algorithm Overview Background Extraction Binary image Stretching Transformation Blue pixels Red pixels Original Image Blue Extraction Red Extraction Calculate: Cell number Area Percentage Dilation/Erosion intersect dilate

11. Graphical User Interface  User selects Magnification of Image Different algorithm for different magnificationDifferent algorithm for different magnification  User selects area of image or entire image to be processed  Results are displayed in graphical form  Results include: Cell areaCell area Cell countCell count PercentagePercentage

12. Questions?