High Performance Adaptive Fidelity Algorithms for Multi-Modality Optic Nerve Head Image Fusion

Authors:
Hua Cao;Nathan Brener;Bahram Khoobehi;S. Sitharama Iyengar
Affiliations:
Computer Science Department, Louisiana State University, Baton Rouge, USA 70802;Computer Science Department, Louisiana State University, Baton Rouge, USA 70802;Department of Ophthalmology, LSU Eye Center, New Orleans, USA 70112;Computer Science Department, Louisiana State University, Baton Rouge, USA 70802
Venue:
Journal of Signal Processing Systems
Year:
2011

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Abstract

A high performance adaptive fidelity approach for multi-modality Optic Nerve Head (ONH) image fusion is presented. The new image fusion method, which consists of the Adaptive Fidelity Exploratory Algorithm (AFEA) and the Heuristic Optimization Algorithm (HOA), is reliable and time efficient. It has achieved an optimal fusion result by giving the visualization of fundus image with a maximum angiogram overlay. Control points are detected at the vessel bifurcations using the AFEA. Shape similarity criteria are used to match the control points that represent same salient features of different images. HOA adjusts the initial good-guess of control points at the sub-pixel level in order to maximize the objective function Mutual-Pixel-Count (MPC). In addition, the performance of the AFEA and HOA algorithms was compared to the Centerline Control Point Detection Algorithm, Root Mean Square Error (RMSE) minimization objective function employed by the traditional Iterative Closest Point (ICP) algorithm, Genetic Algorithm, and some other existing image fusion approaches. The evaluation results strengthen the AFEA and HOA algorithms in terms of novelty, automation, accuracy, and efficiency.