Efficient Terrain Matching With 3-D Zernike Moments

Wang, Kedong; Zhu, Tongqian; Gao, Yifeng; Wang, Jinling

doi:10.1109/taes.2018.2849921

Cited by 12 publications

(10 citation statements)

References 28 publications

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“…These 3DZMs were later described as shape descriptors for shape retrieval ( Novotni and Klein, 2004 ). Canterakis’ algorithm has been applied to terrain matching ( Wang et al, 2018a , b ) and protein–protein interface prediction ( Daberdaku and Ferrari, 2018 ). However, Canterakis’ algorithm could only be used to compute ZM up to the order of 25, due to computational demand and instability.…”

Section: Methods and Resultsmentioning

confidence: 99%

Brain white matter hyperintensity lesion characterization in 3D T2 fluid-attenuated inversion recovery magnetic resonance images: Shape, texture, and their correlations with potential growth

Gwo

Zhu²,

Zhang³

2022

Front. Neurosci.

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Analyses of age-related white matter hyperintensity (WMH) lesions manifested in T2 fluid-attenuated inversion recovery (FLAIR) magnetic resonance images (MRI) have been mostly on understanding the size and location of the WMH lesions and rarely on the morphological characterization of the lesions. This work extends our prior analyses of the morphological characteristics and texture of WMH from 2D to 3D based on 3D T2 FLAIR images. 3D Zernike transformation was used to characterize WMH shape; a fuzzy logic method was used to characterize the lesion texture. We then clustered 3D WMH lesions into groups based on their 3D shape and texture features. A potential growth index (PGI) to assess dynamic changes in WMH lesions was developed based on the image texture features of the WMH lesion penumbra. WMH lesions with various sizes were segmented from brain images of 32 cognitively normal older adults. The WMH lesions were divided into two groups based on their size. Analyses of Variance (ANOVAs) showed significant differences in PGI among WMH shape clusters (P = 1.57 × 10–3 for small lesions; P = 3.14 × 10–2 for large lesions). Significant differences in PGI were also found among WMH texture group clusters (P = 1.79 × 10–6). In conclusion, we presented a novel approach to characterize the morphology of 3D WMH lesions and explored the potential to assess the dynamic morphological changes of WMH lesions using PGI.

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Section: Methods and Resultsmentioning

confidence: 99%

Brain white matter hyperintensity lesion characterization in 3D T2 fluid-attenuated inversion recovery magnetic resonance images: Shape, texture, and their correlations with potential growth

Gwo

Zhu²,

Zhang³

2022

Front. Neurosci.

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show abstract

“…These 3DZMs were later described as shape descriptors for shape retrieval (Novotni & Klein, 2004). Canterakis' algorithm has been applied to terrain matching (Wang et al, 2018(Wang et al, , 2019 and protein-protein interface prediction (Daberdaku & Ferrari, 2018). However, Canterakis' algorithm could only be used to compute ZM up to the order of 25, due to computational demand and instability.…”

Section: D Zernike Transformationmentioning

confidence: 99%

Hippocampus shape characterization with 3D Zernike transformation in clinical Alzheimer's disease progression

Zhu

Gwo

Deng

et al. 2022

Human Brain Mapping

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Alzheimer's disease (AD) is a neurodegenerative disease and the most common cause of dementia among older adults. Mild cognitive impairment (MCI) is considered a transitional phase between healthy cognitive aging and dementia. Progressive brain volume reduction/atrophy, particularly of the hippocampus, is associated with the transition from normal to MCI, and then to AD. We aimed to develop methods to characterize the shape of hippocampus and explore its potential as an imaging marker to monitor clinical AD progression. We implemented a 3D Zernike transformation to characterize the shape changes of hippocampus in 428 older subjects with high‐quality T 1 ‐weighted volumetric brain scans from the Alzheimer's Disease Neuroimaging Initiative data set (151 normal, 258 MCI, and 19 AD). Over 2 years, 15 cognitively normal subjects converted to MCI, and 42 subjects with MCI converted to AD. We found a significant correlation between hippocampal volume changes and Zernike shape metrics. Before a clinical diagnosis of AD, the shapes of the left and right hippocampi changed slowly. After AD diagnosis, both volume and shape changed rapidly but were uncorrelated to each other. During the transition from a clinical diagnosis of MCI to AD, the shape of the left and right hippocampi changed in a correlated manner but became uncorrelated after AD diagnosis. Finally, the pace of hippocampus shape change was associated with its shape and the subject's age and disease condition. In conclusion, the hippocampus shape features characterized with 3D Zernike transformation, in complement to volume measures, may serve as a novel imaging marker to monitor clinical AD progression.

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“…Terrain contour-matching [8] tries to find a position that best matches the DTM according to some specific rules, and is regarded as a typical batch method. Batch-matching algorithms based on some invariant features can be performed efficiently with stability and reliability, such as Hu moments [9] and Zernike moments [10]. The uncertainty of the position estimate is unavailable, and the performance cannot be judged accordingly.…”

Section: Introductionmentioning

confidence: 99%

Particle filter underwater terrain-aided navigation based on gradient fitting

Zhou¹,

Wang²,

Gao³

et al. 2022

Meas. Sci. Technol.

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The Inertial Navigation System (INS) is widely used for Autonomous Underwater Vehicles (AUVs) navigation with excellent short-term precision. However, the positioning error of INS accumulates along with time. It is imperative to appeal to other approaches to mitigate drift errors, especially for long-term sailing tasks. To that end, underwater Terrain Based Navigation (TBN) is effective in bounding the drift errors. In particular, the particle filtering (PF) method is extensively employed in TBN to tackle the highly nonlinear measurement equation. In our previous work, the statistical properties of the Digital Terrain Model (DTM) gradient were exploited, and we proposed to use three probability density functions (PDFs) to characterize the normalized data, i.e., Gaussian, Gamma and Weibull distributions. In this paper, the likelihood was modified according to the gradient fitting results, i.e., an optimal distribution selection. Moreover, to prevent the measurement data with large gradients from generating particles with lower weights, a pre-screen procedure was proposed to stabilize PF sampling. As shown and demonstrated in simulations, our proposed improved PF method outperforms the other comparative ones in terms of Root Mean Square Error (RMSE) and stability, as well as accuracy, especially for flat terrain data. In terms of computation cost, employing smaller amount of measurement data, the proposed method is faster than the standard PF method.

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Efficient Terrain Matching With 3-D Zernike Moments

Cited by 12 publications

References 28 publications

Brain white matter hyperintensity lesion characterization in 3D T2 fluid-attenuated inversion recovery magnetic resonance images: Shape, texture, and their correlations with potential growth

Brain white matter hyperintensity lesion characterization in 3D T2 fluid-attenuated inversion recovery magnetic resonance images: Shape, texture, and their correlations with potential growth

Hippocampus shape characterization with 3D Zernike transformation in clinical Alzheimer's disease progression

Particle filter underwater terrain-aided navigation based on gradient fitting

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