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

Computer-Aided Detection 11/22/2018

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. 11/22/2018

How We Became Expert Mammographer’s

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. 11/22/2018

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) 11/22/2018

Macrocalcifications and Microcalcifications Left vs Right Old vs New Macrocalcifications and Microcalcifications Lesions 11/22/2018

Where We Got Started With The Code

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. 11/22/2018

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

Computer Vision

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) 11/22/2018

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." 11/22/2018

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 11/22/2018

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. 11/22/2018

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

We Have Stats! Calcification Search: Invert Lump 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 11/22/2018

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. 11/22/2018

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

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 11/22/2018

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

FIN