Search Results for author: Moo. K. Chung

Found 13 papers, 0 papers with code

Introduction to logistic regression

no code implementations28 Aug 2020 Moo. K. Chung

For random field theory based multiple comparison corrections In brain imaging, it is often necessary to compute the distribution of the supremum of a random field.

General Classification Two-sample testing

Gaussian kernel smoothing

no code implementations19 Jul 2020 Moo. K. Chung

Then from the central limit theorem, the weighted average should be more Gaussian.

Image Registration image smoothing

Fast Polynomial Approximation of Heat Kernel Convolution on Manifolds and Its Application to Brain Sulcal and Gyral Graph Pattern Analysis

no code implementations7 Nov 2019 Shih-Gu Huang, Ilwoo Lyu, Anqi Qiu, Moo. K. Chung

We also derive the closed-form expression of the spectral decomposition of the Laplace-Beltrami operator and use it to solve heat diffusion on a manifold for the first time.

Automatic Identification of Twin Zygosity in Resting-State Functional MRI

no code implementations30 Jun 2018 Andrey Gritsenko, Martin A. Lindquist, Gregory R. Kirk, Moo. K. Chung

A key strength of twin studies arises from the fact that there are two types of twins, monozygotic and dizygotic, that share differing amounts of genetic information.

Variable Selection

Heat Kernel Smoothing in Irregular Image Domains

no code implementations21 Oct 2017 Moo. K. Chung, Yanli Wang, Gurong Wu

We present the discrete version of heat kernel smoothing on graph data structure.

Mapping Heritability of Large-Scale Brain Networks with a Billion Connections {\em via} Persistent Homology

no code implementations15 Sep 2015 Moo. K. Chung, Victoria Vilalta-Gil, Paul J. Rathouz, Benjamin B. Lahey, David H. Zald

In many human brain network studies, we do not have sufficient number (n) of images relative to the number (p) of voxels due to the prohibitively expensive cost of scanning enough subjects.

Statistical Inference Models for Image Datasets With Systematic Variations

no code implementations CVPR 2015 Won Hwa Kim, Barbara B. Bendlin, Moo. K. Chung, Sterling C. Johnson, Vikas Singh

Statistical analysis of longitudinal or cross sectionalbrain imaging data to identify effects of neurodegenerative diseases is a fundamental task in various studies in neuroscience.

Unified Heat Kernel Regression for Diffusion, Kernel Smoothing and Wavelets on Manifolds and Its Application to Mandible Growth Modeling in CT Images

no code implementations23 Sep 2014 Moo. K. Chung, Anqi Qiu, Seongho Seo, Houri K. Vorperian

Starting with the heat kernel constructed from the eigenfunctions, we formulate a new bivariate kernel regression framework as a weighted eigenfunction expansion with the heat kernel as the weights.

Persistent Homology in Sparse Regression and Its Application to Brain Morphometry

no code implementations31 Aug 2014 Moo. K. Chung, Jamie L. Hanson, Jieping Ye, Richard J. Davidson, Seth D. Pollak

Sparse systems are usually parameterized by a tuning parameter that determines the sparsity of the system.

Diffeomorphic Metric Mapping and Probabilistic Atlas Generation of Hybrid Diffusion Imaging based on BFOR Signal Basis

no code implementations25 Sep 2013 Jia Du, A. Pasha Hosseinbor, Moo. K. Chung, Barbara B. Bendlin, Gaurav Suryawanshi, Andrew L. Alexander, Anqi Qiu

In this work, we show that the reorientation of the $q$-space signal due to spatial transformation can be easily defined on the BFOR signal basis.

Multi-resolution Shape Analysis via Non-Euclidean Wavelets: Applications to Mesh Segmentation and Surface Alignment Problems

no code implementations CVPR 2013 Won Hwa Kim, Moo. K. Chung, Vikas Singh

In this paper, we adapt recent results in harmonic analysis, to derive NonEuclidean Wavelets based algorithms for a range of shape analysis problems in vision and medical imaging.

Wavelet based multi-scale shape features on arbitrary surfaces for cortical thickness discrimination

no code implementations NeurIPS 2012 Won H. Kim, Deepti Pachauri, Charles Hatt, Moo. K. Chung, Sterling Johnson, Vikas Singh

In contrast to hypothesis tests on point-wise measurements, in this paper, we make the case for performing statistical analysis on multi-scale shape descriptors that characterize the local topological context of the signal around each surface vertex.

Two-sample testing

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