Ultrasound biomicroscopy glaucoma images analysis based on rough set and pulse coupled neural network

El-Sayed A. El-Dahshan

Aboul-Ella Hassanien

pp. 275-293

in: Ajith Abraham, Francisco Herrera, Aboul-Ella Hassanien (eds), Foundations of computational intelligence volume 2, Berlin, Springer, 2009

Abstract

The objective of this book chapter is to present the rough sets and pulse coupled neural network scheme for Ultrasound Biomicroscopy glaucoma images analysis. To increase the efficiency of the introduced scheme, an intensity adjustment process is applied first using the Pulse Coupled Neural Network (PCNN) with a median filter. This is followed by applying the PCNN-based segmentation algorithm to detect the boundary of the interior chamber of the eye image. Then, glaucoma clinical parameters have been calculated and normalized, followed by application of a rough set analysis to discover the dependency between the parameters and to generate set of reduct that contains minimal number of attributes. Finally, a rough confusion matrix is designed for discrimination to test whether they are normal or glaucomatous eyes. Experimental results show that the introduced scheme is very successful and has high detection accuracy.