Name
Abstract | ||
|---|---|---|
We propose a new technique for automatic spinal deformity detection from moire topographic images. Normally the moire stripes of a human body show a symmetric pattern. According to the progress of the deformity of a spine, asymmetry becomes larger. Numerical representation of the degree of asymmetry is therefore useful in evaluating the deformity. Displacement of local centroids and difference of gray value are calculated between the left-hand side and the right-hand side regions of the moire images with respect to the extracted middle line. Extracted 4 feature vectors (mean value and standard deviation from the each displacement) from the left-hand side and right-hand side rectangle areas apply to train a neural network. An experiment was performed employing 1,200 real moire images and 90.3% of the images were classified correctly. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1007/11552499_79 | ICAPR (2) |
Keywords | Field | DocType |
right-hand side rectangle area,moire image,neural network,mean value,gray value,right-hand side region,moire topographic image,real moire image,left-hand side,automatic spinal deformity detection,moire stripe,human body,back propagation,feature vector,standard deviation | Moiré pattern,Computer vision,Feature vector,Image sensor,Pattern recognition,Computer science,Rectangle,Artificial intelligence,Artificial neural network,Standard deviation,Centroid,Deformity | Conference |
Volume | ISSN | ISBN |
3687 | 0302-9743 | 3-540-28833-3 |
Citations | PageRank | References |
0 | 0.34 | 4 |
Authors | ||
7 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hyoungseop Kim | 1 | 293 | 36.05 |
| Joo Kooi Tan | 2 | 105 | 29.88 |
| Seiji Ishikawa | 3 | 342 | 49.06 |
| Marzuki Khalid | 4 | 0 | 0.34 |
| Max Viergever | 5 | 72 | 5.76 |
| Yoshinori Otsuka | 6 | 12 | 3.75 |
| Takashi Shinomiya | 7 | 6 | 3.38 |