Medical-Image-Processing-Final-Project

Implement of Semi-Automated Detection of Breast Mass Spiculation Using Active Contour

Environment

Qt 5.5 (Visual Studio 2013) + OpenCV 3.0

Dataset

http://marathon.csee.usf.edu/Mammography/Database.html

1. C_0013_1.RIGHT_MLO
2. B_3084_1.RIGHT_MLO
3. B_3010_1.RIGHT_MLO
4. B_3071_1.LEFT_MLO
5. B_3021_1.LEFT_MLO
6. B_3017_1.LEFT_MLO

Reference

1. Boonthong, P., Jantarakongkul, B., Rasmequan, S., Rodtook, A., Chinnasarn, K. (2014). Semi-Automated Detection of Breast Mass Spiculation Using Active Contour. Asia-Pacific Signal and Information Processing Association, 2014 Annual Summit and Conference (APSIPA). Available at: https://www.researchgate.net/publication/234838692_Hill-climbing_Algorithm_for_Efficient_Color-based_Image_Segmentation
2. Hill-climbing Algorithm for Efficient Color-based Image Segmentation
3. OpenCV Documentation. Available at: http://docs.opencv.org/
4. Fuzzy c-means clustering. Available at: https://github.com/shingt/FuzzyCMeans
5. Active Contour Model based on GVF. Available at: https://github.com/HiDiYANG/ActiveContour
6. University of South Florida Digital Mammography Home Page. Available at: http://marathon.csee.usf.edu/Mammography/Database.html
7. LJPEG to PNG Utility. Available at: http://stackoverflow.com/questions/13365587/getting-data-from-digital-database-for-screening-mammography-ddsm