Analysis of brain activation patterns using a 3-D scale-space primal sketch

Lindeberg, Tony; Lidberg, Pär; Roland, Per E.

doi:10.1002/(sici)1097-0193(1999)7:3<166::aid-hbm3>3.0.co;2-i

Cited by 23 publications

(27 citation statements)

References 53 publications

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“…One might consider integrating the TFCE score not just over the cluster-forming height/extent in the way proposed, but also integrating over a second-dimension -data smoothing extent. Although we have tried to find an optimal data smoothing extent, for the reasons discussed above, there may still be value in searching/integrating over a range of smoothings, as suggested in scale-space and wavelet literature going back more than a decade [Worsley et al, 1996a, Lindeberg et al, 1999, Coulon et al, 2000, Fadili and Bullmore, 2004, Flandin and Penny, 2007, Van De Ville et al, 2007. Further investigations in these directions will be the subject of future work.…”

Section: Discussionmentioning

confidence: 99%

Threshold-free cluster enhancement: Addressing problems of smoothing, threshold dependence and localisation in cluster inference

2009

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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 to finding true signal than voxelwise 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) clusterforming 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 voxelwise 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.

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Section: Discussionmentioning

confidence: 99%

Threshold-free cluster enhancement: Addressing problems of smoothing, threshold dependence and localisation in cluster inference

2009

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“…Medical applications of the scale-space primal sketch have been developed for analyzing functional brain activation images (Lindeberg et al [117], Coulon et al [29], Rosbacke et al [136], Mangin et al [120]) and for capturing the folding patterns of the cortical surface (Cachia et al [25]). More algorithmically based work on building graphs of blob and ridge features at different scales was presented by Crowley and his co-workers [31,32] using difference of low-pass features defined from a pyramid; hence with very close similarities to differencesof-Gaussians operators and thus the Laplacian.…”

Section: Related Workmentioning

confidence: 99%

Image Matching Using Generalized Scale-Space Interest Points

Lindeberg

2014

J Math Imaging Vis

Self Cite

152

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The performance of matching and object recognition methods based on interest points depends on both the properties of the underlying interest points and the choice of associated image descriptors. This paper demonstrates advantages of using generalized scale-space interest point detectors in this context for selecting a sparse set of points for computing image descriptors for image-based matching. For detecting interest points at any given scale, we make use of the Laplacian ∇ 2 norm L, the determinant of the Hessian det H norm L and four new unsigned or signed Hessian feature strength measures D 1,norm L,D 1,norm L, D 2,norm L and D 2,norm L, which are defined by generalizing the definitions of the Harris and Shi-and-Tomasi operators from the second moment matrix to the Hessian matrix. Then, feature selection over different scales is performed either by scale selection from local extrema over scale of scale-normalized derivates or by linking features over scale into feature trajectories and computing a significance measure from an integrated measure of normalized feature strength over scale. A theoretical analysis is presented of the robustness of the differential entities underlying these interest points under image deformations, in terms of invariance properties under affine image deformations or approximations thereof. Disregarding the effect of the rotationally symmetric scale-space smoothing operation, the determinant of the Hessian det H norm L is a truly affine covariant differential entity and the Hessian feature strength measures D 1,norm L andD 1,norm L have a major contribution from the affine covariant determinant of the Hessian, implying that local extrema of these differen- It is shown how these generalized scale-space interest points allow for a higher ratio of correct matches and a lower ratio of false matches compared to previously known interest point detectors within the same class. The best results are obtained using interest points computed with scale linking and with the new Hessian feature strength measures D 1,norm L,D 1,norm L and the determinant of the Hessian det H norm L being the differential entities that lead to the best matching performance under perspective image transformations with significant foreshortening, and better than the more commonly used Laplacian operator, its difference-of-Gaussians approximation or the Harris-Laplace operator. We propose that these generalized scale-space interest points, when accompanied by associated local scale-invariant image descriptors, should allow for better performance of interest point based methods for image-based matching, object recognition and related visual tasks.

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“…47 The typical choice is a low-pass Gaussian filter in the spatial domain, which smooths high frequency variation in the data. Several researchers have suggested that the use of Gaussian filters for denoising in the spatial domain introduces unwanted biases, [48][49][50][51][52][53] and that the optimal filter width is a function of the size of activation foci, 50,54 which cannot generally be determined a priori except possibly by reference to underlying neuroanatomy. The low-pass filter approach will also blur and displace activated areas and remove the areas of least activation, reducing spatial resolution.…”

Section: Functional Imaging Of the Brainmentioning

confidence: 99%

Data mining in brain imaging

Megalooikonomou¹,

Ford²,

Shen³

et al. 2000

stat methods med res

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Data mining in brain imaging is an emerging field of high importance for providing prognosis, treatment, and a deeper understanding of how the brain functions. The field of data mining addresses the question of how best to use this data to discover new knowledge and improve the process of decision making. The discovery of associations between human brain structures and functions (i.e. human brain mapping) has been recognized as the main goal of the Human Brain Project, 1 which is a high-priority project funded by several government initiatives. Mining problems can be grouped in three categories: 2 identifying classifications, finding sequential patterns, and discovering associations. Although data mining is a powerful knowledge discovery technique, there are constraints in the way it can be applied: it is applicationdependent, different applications usually require different mining techniques, and data must be of a certain size and format.3 In this paper we survey current mining methods, give a critical review of the main computational obstacles that lie behind our ability to perform automatic data mining on brain imaging and propose some solutions.There are various problems in mining of brain images that need to be addressed. The first problem is that most fundamental mining algorithms (rule-based learning systems, neural networks, decision trees, Bayesian networks, logistic regressions, and so on), which have been used with great success in medicine, assume that data sets contain only simple numeric and symbolic entries. It is important, therefore, to Data mining in brain imaging is proving to be an effective methodology for disease prognosis and prevention. This, together with the rapid accumulation of massive heterogeneous data sets, motivates the need for efficient methods that filter, clarify, assess, correlate and cluster brain-related information. Here, we present data mining methods that have been or could be employed in the analysis of brain images. These methods address two types of brain imaging data: structural and functional. We introduce statistical methods that aid the discovery of interesting associations and patterns between brain images and other clinical data. We consider several applications of these methods, such as the analysis of taskactivation, lesion-deficit, and structure morphological variability; the development of probabilistic atlases; and tumour analysis. We include examples of applications to real brain data. Several data mining issues, such as that of method validation or verification, are also discussed.

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Analysis of brain activation patterns using a 3-D scale-space primal sketch

Cited by 23 publications

References 53 publications

Threshold-free cluster enhancement: Addressing problems of smoothing, threshold dependence and localisation in cluster inference

Threshold-free cluster enhancement: Addressing problems of smoothing, threshold dependence and localisation in cluster inference

Image Matching Using Generalized Scale-Space Interest Points

Data mining in brain imaging

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