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 detailpreserving 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.
| Original language | English |
|---|---|
| Title of host publication | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
| Publisher | Springer Verlag |
| Pages | 255-262 |
| Number of pages | 8 |
| Volume | 9350 |
| ISBN (Print) | 9783319245706, 9783319245706, 9783319245706 |
| DOIs | |
| Publication status | Published - 2015 |
| Externally published | Yes |
| Event | 18th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2015 - Munich, Germany Duration: 2015 Oct 5 → 2015 Oct 9 |
Publication series
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Volume | 9350 |
| ISSN (Print) | 03029743 |
| ISSN (Electronic) | 16113349 |
Other
| Other | 18th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2015 |
|---|---|
| Country | Germany |
| City | Munich |
| Period | 15/10/5 → 15/10/9 |
Fingerprint
ASJC Scopus subject areas
- Computer Science(all)
- Theoretical Computer Science
Cite this
Space-frequency detail-preserving construction of neonatal brain atlases. / Zhang, Yuyao; Shi, Feng; Yap, Pew Thian; Shen, Dinggang.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 9350 Springer Verlag, 2015. p. 255-262 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9350).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Space-frequency detail-preserving construction of neonatal brain atlases
AU - Zhang, Yuyao
AU - Shi, Feng
AU - Yap, Pew Thian
AU - Shen, Dinggang
PY - 2015
Y1 - 2015
N2 - 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 detailpreserving 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.
AB - 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 detailpreserving 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.
UR - http://www.scopus.com/inward/record.url?scp=84950979621&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84950979621&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-24571-3_31
DO - 10.1007/978-3-319-24571-3_31
M3 - Conference contribution
AN - SCOPUS:84950979621
SN - 9783319245706
SN - 9783319245706
SN - 9783319245706
VL - 9350
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 255
EP - 262
BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PB - Springer Verlag
ER -