Date of Award
Master of Engineering Science
School of Engineering and Mathematics
Faculty of Computing, Health and Science
Evolving from neuro-biological insights, neural network technology gives a computer system an amazing capacity to actually generate decisions dynamically. However, as the amount of data to be processed increases, there is a demand for developing new types of networks such as Cellular Neural Networks (CNN), to ease the computational burden without compromising the outcomes. The objective of this thesis is to research the capability of Shunting Inhibitory Cellular Neural Networks (SICNN) to solve the clarity problems in ultrasound imaging. In this thesis, we begin by reviewing a number of traditional enhancement techniques and measures. Since the entire work of this thesis is based upon a particular model of the CNN, we present a brief review of CNN theory and its applications. The SICNN biological inspiration, derivation and stability issues are reviewed with a view to understand its working principle. We then probe a general study of the feed forward and recurrent SICNN systems. Here, the essential response properties of both SICNN Systems are investigated in depth. The enhancing properties of the recurrent SICNN and its advantages compared to more traditional techniques are also studied. After a thorough investigation into the SICNN response properties, we introduce its application for enhancement in Ultrasound Imaging (UI) modality.
Gogineni, M. M. (2004). Contrast enhancement of ultrasound images using shunting inhibitory cellular neural networks. Retrieved from http://ro.ecu.edu.au/theses/803