Assessment of rotationally-invariant clustering using streamlet tractography

Matthew George Liptrot; Francois Bernard Lauze

Assessment of rotationally-invariant clustering using streamlet tractography

Matthew George Liptrot, Francois Bernard Lauze

Department of Computer Science

Abstract

We present a novel visualisation-based strategy for the assessment of a recently proposed clustering technique for raw DWI volumes which derives rotationally-invariant metrics to classify voxels. The validity of the division of all brain tissue voxels into such classes was assessed using the recently developed streamlets visualisation technique, which aims to represent brain fibres by collections of many short streamlines. Under the assumption that streamlines seeded in a cluster should stay within it, we were able to assess how well perceptual tracing could occur across the boundaries of the clusters.

Original language	English
Publication date	2016
Publication status	Published - 2016
Event	24th Annual Meeting and Exhibition of the International Society for Magnetic Resonance in Medicine - Suntec Singapore Convention & Exhibition Centre, Singapore, Singapore Duration: 7 May 2016 → 13 May 2016 Conference number: 24

Conference

Conference	24th Annual Meeting and Exhibition of the International Society for Magnetic Resonance in Medicine
Number	24
Location	Suntec Singapore Convention & Exhibition Centre
Country/Territory	Singapore
City	Singapore
Period	07/05/2016 → 13/05/2016

Access to Document

http://dev.ismrm.org/2016/3071.html

Cite this

Assessment of rotationally-invariant clustering using streamlet tractography. / Liptrot, Matthew George; Lauze, Francois Bernard.
2016. Abstract from 24th Annual Meeting and Exhibition of the International Society for Magnetic Resonance in Medicine, Singapore, Singapore.

Research output: Contribution to conference › Conference abstract for conference › Research › peer-review

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title = "Assessment of rotationally-invariant clustering using streamlet tractography",

abstract = "We present a novel visualisation-based strategy for the assessment of a recently proposed clustering technique for raw DWI volumes which derives rotationally-invariant metrics to classify voxels. The validity of the division of all brain tissue voxels into such classes was assessed using the recently developed streamlets visualisation technique, which aims to represent brain fibres by collections of many short streamlines. Under the assumption that streamlines seeded in a cluster should stay within it, we were able to assess how well perceptual tracing could occur across the boundaries of the clusters.",

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AU - Lauze, Francois Bernard

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N2 - We present a novel visualisation-based strategy for the assessment of a recently proposed clustering technique for raw DWI volumes which derives rotationally-invariant metrics to classify voxels. The validity of the division of all brain tissue voxels into such classes was assessed using the recently developed streamlets visualisation technique, which aims to represent brain fibres by collections of many short streamlines. Under the assumption that streamlines seeded in a cluster should stay within it, we were able to assess how well perceptual tracing could occur across the boundaries of the clusters.

AB - We present a novel visualisation-based strategy for the assessment of a recently proposed clustering technique for raw DWI volumes which derives rotationally-invariant metrics to classify voxels. The validity of the division of all brain tissue voxels into such classes was assessed using the recently developed streamlets visualisation technique, which aims to represent brain fibres by collections of many short streamlines. Under the assumption that streamlines seeded in a cluster should stay within it, we were able to assess how well perceptual tracing could occur across the boundaries of the clusters.

M3 - Conference abstract for conference

T2 - 24th Annual Meeting and Exhibition of the International Society for Magnetic Resonance in Medicine

Y2 - 7 May 2016 through 13 May 2016

ER -