Name
Title | ||
|---|---|---|
Contralateral subtraction technique for detection of asymmetric abnormalities on whole-body bone scintigrams |
Abstract | ||
|---|---|---|
We developed a computer-aided diagnostic (CAD) scheme for assisting radiologists in the detection of asymmetric abnormalities on a single whole-body bone scintigram by applying a contralateral subtraction (CS) technique. Twenty whole-body bone scans including 107 abnormal lesions in anterior and/or posterior images (the number of lesions per case ranged from I to 16, mean 5.4) were used in this study. In our scheme, the original bone scan image was flipped horizontally to provide a mirror image. The mirror image was first rotated and shifted globally to match the original image approximately, and then was nonlinearly warped by use of an elastic matching technique in order to match the original image accurately. We applied a nonlinear lookup table to convert the difference in pixel values between the original and the warped images to new pixel values for a CS image, in order to enhance dark shadows at the locations of abnormal lesions where uptake of radioisotope was asymmetrically high, and to suppress light shadows of the lesions on the contralateral side. In addition, we applied a CAD scheme for the detection of asymmetric abnormalities by use of rule-based tests and sequential application of artificial neural networks with 25 image features extracted from the original and CS images. The performance of the CAD scheme, which was evaluated by a leave-one-case-out method, indicated an average sensitivity of 80.4% with 3.8 false positives per case. This CAD scheme with the contralateral subtraction technique has the potential to improve radiologists' diagnostic accuracy and could be used for computerized identification of asymmetric abnormalities on whole-body bone scans. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1117/12.708635 | Proceedings of SPIE |
Keywords | Field | DocType |
computer-aided diagnosis,whole-body bone scans,detection,contralateral subtraction,detection,artificial neural networks | CAD,Elastic matching,Computer vision,Lookup table,Feature (computer vision),Computer science,Computer-aided diagnosis,Pixel,Artificial intelligence,Subtraction,False positive paradox | Conference |
Volume | Issue | ISSN |
6514 | 33 | 0277-786X |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Junji Shiraishi | 1 | 19 | 8.08 |
| Qiang Li | 2 | 45 | 12.17 |
| daniel appelbaum | 3 | 0 | 0.34 |
| yonglin pu | 4 | 0 | 0.34 |
| Kunio Doi | 5 | 35 | 8.75 |