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Detecting corpus callosum abnormalities in autism subtype using planar conformal mapping
Author(s) -
Duan Ye,
He Qing,
Yin Xiaotian,
Gu Xianfeng,
Karsch Kevin,
Miles Judith
Publication year - 2010
Publication title -
international journal for numerical methods in biomedical engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.741
H-Index - 63
eISSN - 2040-7947
pISSN - 2040-7939
DOI - 10.1002/cnm.1250
Subject(s) - polygon mesh , conformal map , corpus callosum , metric (unit) , visualization , pattern recognition (psychology) , minimum bounding box , artificial intelligence , planar , mathematics , surface (topology) , shape analysis (program analysis) , autism , computer science , geometry , psychology , biology , anatomy , developmental psychology , image (mathematics) , computer graphics (images) , operations management , economics , static analysis , programming language
A number of studies have documented that autism has a neurobiological basis, but the anatomical extent of these neurobiological abnormalities is largely unknown. In this paper, we apply advanced computational techniques to extract 3D models of the corpus callosum (CC) and subsequently analyze local shape variations in a homogeneous group of autistic children. Besides the traditional volumetric analysis, we explore additional phenotypic traits based on the oriented bounding rectangle of the CC. In shape analysis, a new conformal parameterization is applied in our shape analysis work, which maps the surface onto a planar domain. Surface matching among different individual meshes is achieved by aligning the planar domains of individual meshes. Shape differences of the CC between autistic patients and the controls are computed using Hotelling T 2 two‐sample metric followed by a permutation test. The raw and corrected p ‐values are shown in the results. Additional visualization of the group difference is provided via mean difference map. Copyright © 2009 John Wiley & Sons, Ltd.