Book chapter
A Surface-Based Fractal Information Dimension Method for Cortical Complexity Analysis
Medical Imaging and Augmented Reality, pp.133-141
Lecture Notes in Computer Science, Springer Berlin Heidelberg
2008
DOI: 10.1007/978-3-540-79982-5_15
Abstract
In this paper, we proposed a new surface-based fractal information dimension (FID) method to quantify the cortical complexity. Unlike the traditional box-counting method to measure the capacity dimension, our method is a surface-based fractal information dimension method, which incorporates surface area into the probability calculation and thus encapsulates more information of the original cortical surfaces. The accuracy of the algorithm was validated via experiments on phantoms. With the proposed method, we studied the abnormalities of the cortical complexity of the early blind (EB; n=15), compared with matched controls (n=15). We found significantly increased FIDs in the left occipital lobe and decreased FIDs in the right frontal and right parietal lobe in early blind compared with controls. The results demonstrated the potential of the proposed method for identifying cortical abnormalities.
Details
- Title: Subtitle
- A Surface-Based Fractal Information Dimension Method for Cortical Complexity Analysis
- Creators
- Yuanchao Zhang - National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, People’s Republic of ChinaJiefeng Jiang - National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, People’s Republic of ChinaLei Lin - National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, People’s Republic of ChinaFeng Shi - National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, People’s Republic of ChinaYuan Zhou - National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, People’s Republic of ChinaChunshui Yu - Department of Radiology, Xuanwu Hospital of Capital Medical University, Beijing, People’s Republic of ChinaKuncheng Li - Department of Radiology, Xuanwu Hospital of Capital Medical University, Beijing, People’s Republic of ChinaTianzi Jiang - National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, People’s Republic of China
- Resource Type
- Book chapter
- Publication Details
- Medical Imaging and Augmented Reality, pp.133-141
- Publisher
- Springer Berlin Heidelberg; Berlin, Heidelberg
- Series
- Lecture Notes in Computer Science
- DOI
- 10.1007/978-3-540-79982-5_15
- eISSN
- 1611-3349
- ISSN
- 0302-9743
- Language
- English
- Date published
- 2008
- Academic Unit
- Psychological and Brain Sciences; Iowa Neuroscience Institute
- Record Identifier
- 9984065828302771
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