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Introduction to medical image analysis Final Project Presentation

Introduction to medical image analysis Final Project Presentation. Sang Woo Lee. Problem Definition. Nipple Detection for mammogram Mammogram A specific type of imaging that uses a low-dose x-ray system to examine breasts

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Introduction to medical image analysis Final Project Presentation

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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 opaque • 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

  10. Result

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