Name
Abstract | ||
|---|---|---|
A Support Vector Machine (SVM) based method, Support Vector Clustering, is applied to the problem of modelling 3D objects represented in CT medical images. The method produces accurate surface representations of the objects from data distributed in its volume. This procedure is of advantage in medical imaging since it does not require complicated segmentations and it is shown to be noise robust. There seems to be no limitations regarding the topology of the object to be modelled and a high number of data points can be processed. The method outputs sparse results in the sense that the model is defined in terms of a significant reduced set of data points, achieving great compression rates. |
Year | DOI | Venue |
|---|---|---|
2004 | 10.1109/ISBI.2004.1398848 | 2004 2ND IEEE INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING: MACRO TO NANO, VOLS 1 and 2 |
Keywords | Field | DocType |
surface reconstruction,image segmentation,biomedical imaging,support vector machine,visualization,support vector machines,learning artificial intelligence,computed tomography,interpolation,svm,topology,image reconstruction | Structured support vector machine,Data point,Computer vision,Pattern recognition,Visualization,Medical imaging,Computer science,Support vector machine,Image segmentation,Artificial intelligence,Support vector clustering | Conference |
Citations | PageRank | References |
2 | 0.57 | 4 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Cristina García | 1 | 19 | 3.56 |
| José Alí Moreno | 2 | 65 | 8.60 |