Open AccessFAST DETECTION OF MASSES IN MAMMOGRAMS WITH DIFFICULT CASE EXCLUSION
Author(s) -
Gábor Takács,
Béla Pataki
Publication year - 2014
Publication title -
computing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.184
H-Index - 11
eISSN - 2312-5381
pISSN - 1727-6209
DOI - 10.47839/ijc.4.3.365
Subject(s) - mammography , computer science , cancer detection , breast cancer , artificial intelligence , simple (philosophy) , pattern recognition (psychology) , cancer , algorithm , computer vision , medicine , philosophy , epistemology
Breast cancer is one of the most common forms of cancer among women. Currently mammography is the most efficient method for early detection. A simple and fast mammographic mass detection system and two different methods for difficult case exclusion are presented in this paper. The mass detection system uses a modified version of a known algorithm for small masses and a new algorithm for large masses. The first difficult case filtering method is based on tissue density estimation, the second one on mass candidate count. The system was tested with 600 mammographic cases, each containing 4 images. Case-level performance was measured for malignant mass detection first without and then with difficult case exclusion.