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C.A.M.P.

C.A.M.P. . By: Josh Coats & Jen Eckrote. Computer-Aided Detection . Currently . Specific algorithms analyze radiographical features Image processing (IP): Enhances features of interest and de-enhances others with filters.

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C.A.M.P.

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  1. C.A.M.P. By: Josh Coats & Jen Eckrote

  2. Computer-Aided Detection

  3. Currently • Specific algorithms analyze radiographical features • Image processing (IP): • Enhances features of interest and de-enhances others with filters. • Quantify visual features for providing geometric, topologic and others. • Artificial Intelligence (AI): • Make decisions regarding features the radiologists should be alerted to.

  4. How We Became Expert Mammographer’s

  5. J&J’s Guide To Mammography • Medio-Lateral Oblique (MLO) • Medio: center • Lateral: outside • Cranio-Caudal (CC) • Cranio: head • Caudal: tail **note there are always at least 2 of each view because of both breasts.

  6. What Do Radiologists Look For? • Differences between the Right and Left Breasts • Differences between old and new mammograms • Abnormal Lesions other than old surgical scars • Calcium Deposits (aka Calcifications)

  7. Left vs Right Old vs New Macrocalcifications and Microcalcifications Lesions

  8. Where We Got Started With The Code

  9. Popular Means of Manipulation • Convolution Filters: obtain a weighted average of a group or pair of pixels surrounding the one to be manipulated. • Edge Detection: a technique that locates an edge by examining an image for abrupt changes in pixel values. • Hough Transform: feature extraction technique identifying lines, circles and other geometric shapes within an image.

  10. Demo of C.A.M.P. v0.5 Shows array of filters mentioned in use.

  11. Computer Vision

  12. Why It’s Hard To Do • Data Size: because images are billions of pixels it takes a lot of time to look at all of them. • Hope to enable with Segmentation • Resolution: images may seem fuzzy and may contain “bad” pixels (inaccurate display of image as it really is)

  13. What makes Vision Hardest • We live in a 3-D world! • Yet we are trying to allow the computer to "see" this 3-D world with 2-D images • If we could use 3-D images the math in writing the algorithms would be much more complex but there would be more and more things that the computer could use to "see."

  14. Levels of Computer Vision • Digitizing - getting the image in a digital format • which we really aren't that worried about • Low-level processing - threshholding, noise filtering, and edge detection • which we have finished • High-level understanding - object detection, understanding objects, interpreting data • which is what we are still working on

  15. WARNING OUR PROGRAM HAS BEEN DESIGNED TO AID THE RADIOLOGIST. IT IS NOT INTEDED TO TAKE OVER HIS JOB MERELY AS A SECOND SET OF EYES. IT IS TO BE VIEWED ONCE THE DOCTER HAS MADE HIS PRE-LIMINARY DIAGNOSIS AND IS INTEDED TO POINT OUT AREAS OF CONCERN.

  16. Demo of C.A.M.P. v1.0 Computer-Aided Mammography Process

  17. We Have Stats! • Calcification Search: • Found 50%-100% of the calcifications • Invert Lump Search: • Found 78.46% of all abnormalities present in our nearly 100 mammograms • Topographic Lump Search: • Found 80.05% of all abnormalities present in our nearly 100 mammograms

  18. Ain’t Got No Time • We would have implemented a working version of draw_Boxes, so that areas of concern would be boxed. • We also would have liked to add Artificial Intelligence (AI) for computer learning so that we could not only collect data on performance but so that the computer could learn from it as well.

  19. Demo of C.A.M.P. beta v1.1 Forgot to mention we have a beta version in progress J

  20. April Fools • Skin Markers During Mammography • Used to help radiologists identify the nipple, surgical scars, raised moles, or other normal features on the breast • may also be used to alert the radiologist to a breast abnormality that warrants close examination, such as a lump Images Courtesy of Beekley Corporation

  21. To All The Grad Students, Professor Weaver, and Kim J THANK YOU

  22. FIN

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