Chapter title |
Space-Frequency Detail-Preserving Construction of Neonatal Brain Atlases
|
---|---|
Chapter number | 31 |
Book title |
Medical Image Computing and Computer-Assisted Intervention -- MICCAI 2015
|
Published in |
Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention, October 2015
|
DOI | 10.1007/978-3-319-24571-3_31 |
Pubmed ID | |
Book ISBNs |
978-3-31-924570-6, 978-3-31-924571-3
|
Authors |
Yuyao Zhang, Feng Shi, Pew-Thian Yap, Dinggang Shen |
Abstract |
Brain atlases are an integral component of neuroimaging studies. However, most brain atlases are fuzzy and lack structural details, especially in the cortical regions. In particular, neonatal brain atlases are especially challenging to construct due to the low spatial resolution and low tissue contrast. This is mainly caused by the image averaging process involved in atlas construction, often smoothing out high-frequency contents that indicate fine anatomical details. In this paper, we propose a novel framework for detail-preserving construction of atlases. Our approach combines space and frequency information to better preserve image details. This is achieved by performing reconstruction in the space-frequency domain given by wavelet transform. Sparse patch-based atlas reconstruction is performed in each frequency subband. Combining the results for all these subbands will then result in a refined atlas. Compared with existing atlases, experimental results indicate that our approach has the ability to build an atlas with more structural details, thus leading to better performance when used to normalize a group of testing neonatal images. |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 5 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 2 | 40% |
Researcher | 1 | 20% |
Unknown | 2 | 40% |
Readers by discipline | Count | As % |
---|---|---|
Computer Science | 1 | 20% |
Neuroscience | 1 | 20% |
Engineering | 1 | 20% |
Unknown | 2 | 40% |