Title :
A novel mammographic mass detection approach to combining suprevised and unsuprevised detection algorithms
Author :
Dae Hoe Kim ; Jae Young Choi ; Yong Man Ro
Author_Institution :
Image & Video Syst. Lab., Korea Adv. Inst. of Sci. & Technol. (KAIST), Daejeon, South Korea
fDate :
Sept. 30 2012-Oct. 3 2012
Abstract :
In this paper, we propose the combination of different mass detection algorithms to increase overall mass detection sensitivity for various types of breast masses on mammograms. In particular, supervised and unsupervised mass detection algorithms are effectively combined to maximize complementary effects of both approaches. By combining the aforementioned mass detection algorithms, we can arrive at a combined mass detection approach that makes stronger and accurate detection results. Comparative experiments have been conducted on public mammogram data set. Our results show that the proposed detection system can considerably improve the mass detection sensitivity with relatively small number of false positives, compared to the implementation of using only a single detection solution.
Keywords :
cancer; mammography; medical image processing; object detection; mammographic mass detection; mass detection sensitivity; unsuprevised detection algorithms; Breast; Cancer; Databases; Delta-sigma modulation; Detection algorithms; Sensitivity; Shape; Mammography; breast masses; combination; multiple detection; supervised and unsupervised mass detection;
Conference_Titel :
Image Processing (ICIP), 2012 19th IEEE International Conference on
Conference_Location :
Orlando, FL
Print_ISBN :
978-1-4673-2534-9
Electronic_ISBN :
1522-4880
DOI :
10.1109/ICIP.2012.6467495