A novel similarity measure for fiber clustering using longest common subsequence

Authors:
Christian Böhm;Jing Feng;Xiao He;Son T. Mai;Claudia Plant;Junming Shao
Affiliations:
University of Munich, Munich, Germany;University of Munich, Munich, Germany;University of Munich, Munich, Germany;University of Munich, Munich, Germany;Florida State University, Tallahassee, FL, USA;University of Munich, Munich, Germany
Venue:
Proceedings of the 2011 workshop on Data mining for medicine and healthcare
Year:
2011

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Abstract

Diffusion tensor imaging (DTI) is an MRI-based technology in neuroscience which provides a non-invasive way to explore the white matter fiber tracks in the human brain. From DTI, thousands of fibers can be extracted, and thus need to be clustered automatically into anatomically meaningful bundles for further use. In this paper, we focus on the essential question how to provide an efficient and effective similarity measure for the fiber clustering problem. Our novel similarity measure is based on the adapted Longest Common Subsequence method to measure shape similarity between fibers. Moreover, the distance between start and end points of a pair of fibers is also included with the shape similarity to form a unified and exible fiber similarity measure which can effectively capture the similarity between fibers in the same bundles even in noisy conditions. To enhance the efficiency, the lower bounding technique is used to restrict the comparison of two fibers thus saving computational cost. Our new similarity measure is used together with density-based clustering algorithm to segment fibers into groups. Experiments on synthetic and real data sets show the efficiency and effectiveness of our approach compared to other distance-based techniques, namely Dynamic Time Warping (DTW), Mean of Closest Point (MCP) and Hausdorf (HDD) distance.