An adaptive partitioning approach for mining discriminant regions in 3D image data

Megalooikonomou, Vasileios; Kontos, Despina; Pokrajac, Dragoljub; Lazarević, Aleksandar; Obradović, Zoran

doi:10.1007/s10844-007-0043-2

Cited by 4 publications

(5 citation statements)

References 36 publications

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“…Preliminary results have indeed demonstrated this advantage of DRP. Evaluation with a pilot dataset of 3D functional brain images showed that DRP can reduce the number of statistical tests by two orders of magnitude, while also outperforming other commonly used medical image classification techniques [8]; DRP outperformed Maximum-Likelihood classification by 20%, Kullback-Leibler classification by 24%, and a static partitioning classification approach by 24% [8, 27]. Further work is underway to fully evaluate the applicability and the potential advantages of DRP for 3D medical image analysis.…”

Section: Discussionmentioning

confidence: 99%

“…The algorithmic outline of DRP was initially introduced and evaluated primarily with synthetic and realistic images [27, 28]. Preliminary evaluation of DRP with functional brain images showed its potential to be used effectively for medical image analysis; DRP was able to reduce the number of statistical tests by two orders of magnitude compared to pixel-wise statistics, while also improving classification accuracy by 15% [8, 27].…”

Section: Introductionmentioning

confidence: 99%

“…Preliminary evaluation of DRP with functional brain images showed its potential to be used effectively for medical image analysis; DRP was able to reduce the number of statistical tests by two orders of magnitude compared to pixel-wise statistics, while also improving classification accuracy by 15% [8, 27]. Here, we evaluate the feasibility of DRP for anatomical imaging, and particularly for performing morphometric analysis.…”

Section: Introductionmentioning

confidence: 99%

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Morphometric analysis of brain images with reduced number of statistical tests: A study on the gender-related differentiation of the corpus callosum

Kontos

Megalooikonomou

Gee

2009

Artificial Intelligence in Medicine

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SummaryObjective-We evaluate the feasibility of applying dynamic recursive partitioning (DRP), an image analysis technique, to perform morphometric analysis. We apply DRP to detect and characterize discriminative morphometric characteristics between anatomical brain structures from different groups of subjects. Our method reduces the number of statistical tests, commonly required by pixelwise statistics, alleviating the effect of the multiple comparison problem.Methods and Materials-The main idea of DRP is to partition the two-dimensional (2D) image adaptively into progressively smaller sub-regions until statistically significant discriminative regions are detected. The partitioning process is guided by statistical tests applied on groups of pixels. By performing statistical tests on groups of pixels rather than on individual pixels, the number of statistical tests is effectively reduced. This reduction of statistical tests restricts the effect of the multiple comparison problem (i.e. type-I error). We demonstrate an application of DRP for detecting gender-related morphometric differentiation of the corpus callosum. DRP was applied to template deformation fields computed from registered magnetic resonance images of the corpus callosum in order to detect regions of significant expansion or contraction between female and male subjects.Results-DRP was able to detect regions comparable to those of pixel-wise analysis, while reducing the number of required statistical tests up to almost 50%. The detected regions were in agreement with findings previously reported in the literature. Statistically significant discriminative morphological variability was detected in the posterior corpus callosum region, the isthmus and the anterior corpus callosum. In addition, by operating on groups of pixels, DRP appears to be less prone to detecting spatially diffused and isolated outlier pixels as significant.Conclusion-DRP can be a viable approach for detecting discriminative morphometric characteristics among groups of subjects, having the potential to alleviate the multiple comparisons' effect by significantly reducing the number of required statistical tests. 3Present address, Corresponding address:

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Morphometric analysis of brain images with reduced number of statistical tests: A study on the gender-related differentiation of the corpus callosum

Kontos

Megalooikonomou

Gee

2009

Artificial Intelligence in Medicine

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“…The proposed approach utilizes octrees [25][26][27] to split the phantom volume V recursively. The octree-based approach is motivated by a desire to optimize the performance in terms of speed and computational complexity.…”

Section: Iia Simulation Of Breast Tissue Structuresmentioning

confidence: 99%

Optimized generation of high resolution breast anthropomorphic software phantoms

2012

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Purpose: The authors present an efficient method for generating anthropomorphic software breast phantoms with high spatial resolution. Employing the same region growing principles as in their previous algorithm for breast anatomy simulation, the present method has been optimized for computational complexity to allow for fast generation of the large number of phantoms required in virtual clinical trials of breast imaging. Methods: The new breast anatomy simulation method performs a direct calculation of the Cooper's ligaments (i.e., the borders between simulated adipose compartments). The calculation corresponds to quadratic decision boundaries of a maximum a posteriori classifier. The method is multiscale due to the use of octree-based recursive partitioning of the phantom volume. The method also provides user-control of the thickness of the simulated Cooper's ligaments and skin. Results: Using the proposed method, the authors have generated phantoms with voxel size in the range of (25-1000 lm)

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“…[7][8][9][10][11][12][13][14] The anthropomorphic software breast phantom 6 used in this study is based upon recursive partitioning of the simulated breast volume using octrees. [15][16][17] The octree-based simulation allows for fast generation of phantoms with very small voxel size.…”

Section: Software Breast Phantomsmentioning

confidence: 99%

Shape analysis of simulated breast anatomical structures

et al. 2012

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Recent advances in high-resolution 3D breast imaging, namely, digital breast tomosynthesis and dedicated breast CT, have enabled detailed analysis of the shape and distribution of anatomical structures in the breast. Such analysis is critically important, since the projections of breast anatomical structures make up the parenchymal pattern in clinical images which can mask the existing abnormalities or introduce false alarms; the parenchymal pattern is also correlated with the risk of cancer. As a first step towards the shape analysis of anatomical structures in the breast, we have analyzed an anthropomorphic software breast phantom. The phantom generation is based upon the recursive splitting of the phantom volume using octrees, which produces irregularly shaped tissue compartments, qualitatively mimicking the breast anatomy. The shape analysis was performed by fitting ellipsoids to the simulated tissue compartments. The ellipsoidal semi-axes were calculated by matching the moments of inertia of each individual compartment and of an ellipsoid. The distribution of Dice coefficients, measuring volumetric overlap between the compartment and the corresponding ellipsoid, as well as the distribution of aspect ratios, measuring relative orientations of the ellipsoids, were used to characterize various classes of phantoms with qualitatively distinctive appearance. A comparison between input parameters for phantom generation and the properties of fitted ellipsoids indicated the high level of user control in the design of software breast phantoms. The proposed shape analysis could be extended to clinical breast images, and used to inform the selection of simulation parameters for improved realism.

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An adaptive partitioning approach for mining discriminant regions in 3D image data

Cited by 4 publications

References 36 publications

Morphometric analysis of brain images with reduced number of statistical tests: A study on the gender-related differentiation of the corpus callosum

Morphometric analysis of brain images with reduced number of statistical tests: A study on the gender-related differentiation of the corpus callosum

Optimized generation of high resolution breast anthropomorphic software phantoms

Shape analysis of simulated breast anatomical structures

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