Name
Abstract | ||
|---|---|---|
Manifold learning algorithms are proposed to be used in image processing based on their ability in preserving data structures while reducing the dimension and the exposure of data structure in lower dimension. Multi-modal images have the same structure and can be registered together as monomodal images if only structural information is shown. As a result, manifold learning is able to transform multi-modal images to mono-modal ones and subsequently do the registration using mono-modal methods. Based on this application, in this paper novel similarity measures are proposed for multi-modal images in which Laplacian eigenmaps are employed as manifold learning algorithm and are tested against rigid registration of PET/MR images. Results show the feasibility of using manifold learning as a way of calculating the similarity between multimodal images. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/EMBC.2014.6943769 | EMBC |
Keywords | DocType | Volume |
laplacian matrix,pet image registration,multimodal image processing,manifold learning based registration algorithms,mr image registration,learning (artificial intelligence),data structure preservation,matrix algebra,laplacian eigenmaps,similarity measures,biomedical mri,positron emission tomography,monomodal images,similarity measure,image registration,dimension reduction,similarity calculation,manifold learning,medical image processing | Conference | 2014 |
ISSN | Citations | PageRank |
1557-170X | 3 | 0.39 |
References | Authors | |
6 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mohammad Farid Azampour | 1 | 3 | 0.39 |
| Aboozar Ghaffari | 2 | 57 | 5.00 |
| Azam Hamidinekoo | 3 | 19 | 3.48 |
| Emad Fatemizadeh | 4 | 117 | 13.86 |