Technical Program

Paper Detail

Paper:	SU-AM-PS4.6
Session:	Image Segmentation, Retrieval and Analysis
Time:	Sunday, April 9, 10:50 - 12:10
Presentation:	Poster
Title:	Rule-Based Decision-Making Framework for Knowledge-Based Anatomical Landmark Localization (K-BALL)
Authors:	Mohammad-Reza Siadat; Henry Ford Health System
	Hamid Soltanian-Zadeh; Henry Ford Health System
	Kost Elisevich; Henry Ford Health System
Abstract:	K-BALL is a general method for localization of anatomical phenomena of the same origin with natural discrepancies distributed over a reference space, e.g., human brain anatomical structures. In this paper, we focus on information analysis step (2nd step) of K-BALL during which landmarks extracted in its first step are evaluated. We provide a framework in which rules are automatically generated based on estimated and derived models. We show that the rules based on the derived models can improve the overall success rate of K-BALL. Each rule evaluates the extracted points by producing an intermediate confidence factor (ICNF). A total confidence factor is calculated using ICNF’s to facilitate the acceptance or rejection of a set of points as landmarks of interest. Using the rules merely based on the estimated models, simulation study produced an overall success rate of 91.8%. Using the rules based on both of the estimated and derived models, this rate increased to 92.5%.

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