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 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
Data sets From Digital Database for Screening Mammography homepage Used thumbnail images Training set - 15 images Test set – 152 images
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
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
Proposed Method Blob detection A nipple is blob-shaped in mammogram Use 2D Gaussian second derivative filtering with various scale Scale-Normalized Gaussian derivative
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
Result Real error distance Mean – 3.40mm Max – 15.1mm Standard derivation – 3.14 Only 5 images(3%) have larger error than 10mm
Result