Name
Abstract | ||
|---|---|---|
Objective This article describes a computer-based method for the classification of spine scoliosis severity. This is a first step toward
an effective computerized tool to assist general practitioners diagnose spine scoliosis. The method progresses away from Cobb
angles toward pattern and magnitude categorization based upon 3D configurations.
Materials and methods The purpose is to classify spine shapes reconstructed from a pair of calibrated X-ray images into one of three categories,
namely, normal spine, moderate scoliosis, and severe scoliosis. The spine shape is represented by the three-dimensional coordinates
of a sequence of equidistant points sampled by interpolation on the reconstructed spine shape. Classification is carried out
using a self- organizing Kohonen neural network trained using this representation.
Results The tests were performed using a database of 174 spine biplane X-rays. The classification accuracy was 97%.
Conclusion The results demonstrate that classification of 3D spine descriptions by a Kohonen neural network affords a solid basis for
an effective tool to assist clinicians in assessing scoliosis severity. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1007/s11548-008-0163-3 | Int. J. Computer Assisted Radiology and Surgery |
Keywords | Field | DocType |
scoliosis severity · kohonen neural network · classification · 3d spine description,three dimensional,self organization | Categorization,Computer vision,Scoliosis,Physical therapy,Kohonen neural network,Artificial intelligence,Physical medicine and rehabilitation,Classifier (linguistics),Medicine | Journal |
Volume | Issue | ISSN |
3 | 1 | 1861-6429 |
Citations | PageRank | References |
1 | 0.39 | 3 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| N. Mezghani | 1 | 1 | 0.39 |
| R. Chav | 2 | 1 | 0.39 |
| L. Humbert | 3 | 1 | 0.39 |
| S. Parent | 4 | 1 | 0.39 |
| Wafa Skalli | 5 | 63 | 15.33 |
| J. A. de Guise | 6 | 46 | 7.45 |