Chapter title |
Combining surface and fiber geometry: an integrated approach to brain morphology.
|
---|---|
Chapter number | 7 |
Book title |
Medical Image Computing and Computer-Assisted Intervention – MICCAI 2013
|
Published in |
Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention, February 2014
|
DOI | 10.1007/978-3-642-40811-3_7 |
Pubmed ID | |
Book ISBNs |
978-3-64-240810-6, 978-3-64-240811-3
|
Authors |
Savadjiev P, Rathi Y, Bouix S, Smith AR, Schultz RT, Verma R, Westin CF, Peter Savadjiev, Yogesh Rathi, Sylvain Bouix, Alex R. Smith, Robert T. Schultz, Ragini Verma, Carl-Fredrik Westin, Savadjiev, Peter, Rathi, Yogesh, Bouix, Sylvain, Smith, Alex R., Schultz, Robert T., Verma, Ragini, Westin, Carl-Fredrik |
Abstract |
Despite the fact that several theories link cortical development and function to the development of white matter and its geometrical structure, the relationship between gray and white matter morphology has not been widely researched. In this paper, we propose a novel framework for investigating this relationship. Given a set of fiber tracts which connect to a particular cortical region, the key idea is to compute two scalar fields that represent geometrical characteristics of the white matter and of the surface of the cortical region. The distributions of these scalar values are then linked via Mutual Information, which results in a quantitative marker that can be used in the study of normal and pathological brain structure and development. We apply this framework to a population study on autism spectrum disorder in children. |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 1 | 3% |
Unknown | 38 | 97% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 10 | 26% |
Student > Ph. D. Student | 9 | 23% |
Student > Bachelor | 4 | 10% |
Student > Master | 4 | 10% |
Student > Doctoral Student | 1 | 3% |
Other | 4 | 10% |
Unknown | 7 | 18% |
Readers by discipline | Count | As % |
---|---|---|
Neuroscience | 8 | 21% |
Computer Science | 6 | 15% |
Medicine and Dentistry | 4 | 10% |
Engineering | 3 | 8% |
Psychology | 3 | 8% |
Other | 5 | 13% |
Unknown | 10 | 26% |