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Introduction to medical image analysis Final Project Presentation Sang Woo Lee.

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1 Introduction to medical image analysis Final Project Presentation Sang Woo Lee

2 Problem Definition  Nipple Detection for mammogram  Mammogram  A specific type of imaging that uses a low-dose x-ray system to examine breasts  Used to aid in the early detection and diagnosis of breast diseases in women  Location of the nipple  Important for registration as a reference point

3 Data sets  From Digital Database for Screening Mammography homepage  Used thumbnail images  Training set - 15 images  Test set – 152 images

4 Proposed Methods  Many algorithms using profile contour  Profile contour is usually extracted manually  Prof. Wesley Snyder  Automatic Contour Extraction  Used “GrowCut” graph cut algorithm  Initial graph setting  Sampled 10x10 kernel center point  Two thresholds – high and low  Foreground - Higher than high threshold  Background - Lower than low  Non-determined - otherwise

5 Proposed Methods  Fat-band  Originally proposed from Petroudi and Brady  The breast edge is composed primarily of fat which appears op aque  Fat-band extraction  Using 3x3 kernel to calculate mean intensity and contrast  Based on mean values of foreground and background  Only used intensity for my project  Contrast seems not to work well

6 Proposed Method  Blob detection  A nipple is blob-shaped in mammogram  Use 2D Gaussian second derivative filtering with various scale  Scale-Normalized Gaussian derivative

7 Proposed Method  Remove unwanted features  Nametag, writing, etc.  Detect nipple location in profile  Use 1D Gaussian second derivative filtering  Detect a protrusion in contours  Use my own heuristic reasoning to detect nipple position  Detect nipple location inside the breast region  Use blob detection  Find relevant blobs to nipple  Final result location  Use my own heuristic reasoning  Based on blob location, and three contour protrusion locations

8 Result  Real error distance  Mean – 3.40mm  Max – 15.1mm  Standard derivation – 3.14  Only 5 images(3%) have larger error than 10mm

9 Result

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