Cytological image analysis with a genetic fuzzy finite state machine.
The objective of this research is to design a pattern recognition system based on a Fuzzy Finite State Machine (FFSM). We try to find an optimal FFSM with Genetic Algorithms (GA). In order to validate this system, the classifier has been applied to a real problem: distinction between normal and abnormal cells in cytological breast fine needle aspirate images and cytological peritoneal fluid images. The characteristic used in the discrimination between normal and abnormal cells is a texture measurement of the chromatin distribution in cellular nuclei. Furthermore, the effectiveness of this method as a pattern classifier is compared with other existing supervised and unsupervised methods and evaluated with Receiver Operating Curves (ROC) methodology.
Estévez
J
J
Departamento de Física Fundamental y Experimental, Electrónica y Sistemas, University of La Laguna, Tenerife, Spain. nacho@cyc.utl.es
Alayón
S
S
Moreno
L
L
Sigut
J
J
Aguilar
R
R
D000465:Algorithms; D017143:Fuzzy Logic; D005823:Genetics; D012372:ROC Curve