Paper Info
Title
Microcalcification detection in mammograms using interval type-2 fuzzy logic system with automatic membership function generation
Abstract
Breast cancer is an important deleterious disease. Mortality rate from this cancer is effectively high and rapidly increasing. The detection at the earlier state can help to reduce the mortality rate. In this paper, we apply the interval type-2 fuzzy system with automatic membership function generation using the Possibilistic C-Means (PCM) clustering algorithm. We utilize four features, i.e., B-descriptor, D-descriptor, average intensity of the inside boundary, and intensity difference between the inside and the outside boundaries. We also compare the result with the result from the interval type-2 fuzzy logic system with automatic membership function generation using the Fuzzy C-Means (FCM) clustering algorithm. The interval type-2 fuzzy system with PCM membership functions generation yields the best result, i.e., 89.47% correct classification with only 6 false positives per image.
Year
DOI
Venue
2010
10.1109/FUZZY.2010.5584896
Fuzzy Systems
Keywords
Field
DocType
cancer,fuzzy logic,mammography,medical image processing,pattern clustering,FCM,PCM,automatic membership function generation,breast cancer,fuzzy c-means,interval type-2 fuzzy logic system,mammograms microcalcification detection,possibilistic c-means
Defuzzification,Fuzzy classification,Pattern recognition,Computer science,Fuzzy logic,Artificial intelligence,Fuzzy control system,Cluster analysis,Fuzzy number,Membership function,Machine learning,False positive paradox
Conference
ISSN
ISBN
Citations 
1098-7584
978-1-4244-6919-2
3
PageRank 
References 
Authors
0.42
9
3
Name
Order
Citations
PageRank
Suraphon Chumklin130.42
S. Auephanwiriyakul224639.45
Nipon Theera-umpon318430.59