Threshold-free cluster enhancement: addressing problems of smoothing, threshold dependence and localisation in cluster inference.
Many image enhancement and thresholding techniques make use of spatial neighbourhood information to boost belief in extended areas of signal. The most common such approach in neuroimaging is cluster-based thresholding, which is often more sensitive than voxel-wise thresholding. However, a limitation...
| Main Authors: | , |
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| Format: | Journal article |
| Language: | English |
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2009
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| _version_ | 1797081763024994304 |
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| author | Smith, S Nichols, T |
| author_facet | Smith, S Nichols, T |
| author_sort | Smith, S |
| collection | OXFORD |
| description | Many image enhancement and thresholding techniques make use of spatial neighbourhood information to boost belief in extended areas of signal. The most common such approach in neuroimaging is cluster-based thresholding, which is often more sensitive than voxel-wise thresholding. However, a limitation is the need to define the initial cluster-forming threshold. This threshold is arbitrary, and yet its exact choice can have a large impact on the results, particularly at the lower (e.g., t, z < 4) cluster-forming thresholds frequently used. Furthermore, the amount of spatial pre-smoothing is also arbitrary (given that the expected signal extent is very rarely known in advance of the analysis). In the light of such problems, we propose a new method which attempts to keep the sensitivity benefits of cluster-based thresholding (and indeed the general concept of "clusters" of signal), while avoiding (or at least minimising) these problems. The method takes a raw statistic image and produces an output image in which the voxel-wise values represent the amount of cluster-like local spatial support. The method is thus referred to as "threshold-free cluster enhancement" (TFCE). We present the TFCE approach and discuss in detail ROC-based optimisation and comparisons with cluster-based and voxel-based thresholding. We find that TFCE gives generally better sensitivity than other methods over a wide range of test signal shapes and SNR values. We also show an example on a real imaging dataset, suggesting that TFCE does indeed provide not just improved sensitivity, but richer and more interpretable output than cluster-based thresholding. |
| first_indexed | 2024-03-07T01:18:35Z |
| format | Journal article |
| id | oxford-uuid:8f8d2ab5-5827-4d58-bf08-9c702442685c |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-07T01:18:35Z |
| publishDate | 2009 |
| record_format | dspace |
| spelling | oxford-uuid:8f8d2ab5-5827-4d58-bf08-9c702442685c2022-03-26T23:05:07ZThreshold-free cluster enhancement: addressing problems of smoothing, threshold dependence and localisation in cluster inference.Journal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:8f8d2ab5-5827-4d58-bf08-9c702442685cEnglishSymplectic Elements at Oxford2009Smith, SNichols, TMany image enhancement and thresholding techniques make use of spatial neighbourhood information to boost belief in extended areas of signal. The most common such approach in neuroimaging is cluster-based thresholding, which is often more sensitive than voxel-wise thresholding. However, a limitation is the need to define the initial cluster-forming threshold. This threshold is arbitrary, and yet its exact choice can have a large impact on the results, particularly at the lower (e.g., t, z < 4) cluster-forming thresholds frequently used. Furthermore, the amount of spatial pre-smoothing is also arbitrary (given that the expected signal extent is very rarely known in advance of the analysis). In the light of such problems, we propose a new method which attempts to keep the sensitivity benefits of cluster-based thresholding (and indeed the general concept of "clusters" of signal), while avoiding (or at least minimising) these problems. The method takes a raw statistic image and produces an output image in which the voxel-wise values represent the amount of cluster-like local spatial support. The method is thus referred to as "threshold-free cluster enhancement" (TFCE). We present the TFCE approach and discuss in detail ROC-based optimisation and comparisons with cluster-based and voxel-based thresholding. We find that TFCE gives generally better sensitivity than other methods over a wide range of test signal shapes and SNR values. We also show an example on a real imaging dataset, suggesting that TFCE does indeed provide not just improved sensitivity, but richer and more interpretable output than cluster-based thresholding. |
| spellingShingle | Smith, S Nichols, T Threshold-free cluster enhancement: addressing problems of smoothing, threshold dependence and localisation in cluster inference. |
| title | Threshold-free cluster enhancement: addressing problems of smoothing, threshold dependence and localisation in cluster inference. |
| title_full | Threshold-free cluster enhancement: addressing problems of smoothing, threshold dependence and localisation in cluster inference. |
| title_fullStr | Threshold-free cluster enhancement: addressing problems of smoothing, threshold dependence and localisation in cluster inference. |
| title_full_unstemmed | Threshold-free cluster enhancement: addressing problems of smoothing, threshold dependence and localisation in cluster inference. |
| title_short | Threshold-free cluster enhancement: addressing problems of smoothing, threshold dependence and localisation in cluster inference. |
| title_sort | threshold free cluster enhancement addressing problems of smoothing threshold dependence and localisation in cluster inference |
| work_keys_str_mv | AT smiths thresholdfreeclusterenhancementaddressingproblemsofsmoothingthresholddependenceandlocalisationinclusterinference AT nicholst thresholdfreeclusterenhancementaddressingproblemsofsmoothingthresholddependenceandlocalisationinclusterinference |