Name
Abstract | ||
|---|---|---|
Template matching is a popular approach to computer-aided detection of brain lesions from magnetic resonance (MR) images. The outcomes are often sufficient for localizing lesions and assisting clinicians in diagnosis. However, processing large MR volumes with three-dimensional (3D) templates is demanding in terms of computational resources, hence the importance of the reduction of computational complexity of template matching, particularly in situations in which time is crucial (e.g. emergent stroke). In view of this, we make use of 3D Gaussian templates with varying radii and propose a new method to compute the normalized cross-correlation coefficient as a similarity metric between the MR volume and the template to detect brain lesions. Contrary to the conventional fast Fourier transform (FFT) based approach, whose runtime grows asOoN log Nthornwith the number of voxels, the proposed method computes the cross-correlation in OoNthorn. We show through our experiments that the proposed method outperforms the FFT approach in terms of computational time, and retains comparable accuracy. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1007/978-3-319-55524-9_6 | Lecture Notes in Computer Science |
Field | DocType | Volume |
Voxel,Template matching,Binary logarithm,Normalization (statistics),Computer science,Algorithm,Gaussian,Fast Fourier transform,Template,Computational complexity theory | Conference | 10154 |
ISSN | Citations | PageRank |
0302-9743 | 0 | 0.34 |
References | Authors | |
1 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Subhranil Koley | 1 | 6 | 1.45 |
| Chandan Chakraborty | 2 | 537 | 50.60 |
| Caterina Mainero | 3 | 81 | 6.17 |
| Fischl Bruce | 4 | 4131 | 219.39 |
| Iman Aganj | 5 | 195 | 18.93 |