Name
Abstract | ||
|---|---|---|
In this paper, we investigate the influence of the clinical context of high–resolution computed tomography (HRCT) images of the chest on tissue classification. Evaluation of the classification performance is based on high–quality visual data extracted from clinical routine. The clinical attributes with highest information gain ratio show to be relevant and consistent for the classification of lung tissue patterns. A combination of visual and clinical attributes allowed a mean of 93% correct predictions of testing instances among the five classes of lung tissue with optimized support vector machines (SVM), which represents a significant benefit of 8% compared to a pure visually–based classification. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1109/CBMS.2008.112 | CBMS |
Keywords | Field | DocType |
lung tissue classification,highest information gain ratio,lung tissue,clinical context,quality visual data,tissue classification,correct prediction,hrct data,lung tissue pattern,clinical routine,clinical attribute,classification performance,support vector machine,image classification,visualization,biomedical imaging,accuracy,image analysis,data integrity,image processing,machine learning,data analysis,image retrieval,computed tomography,testing,information gain,support vector machines | Computer vision,Pattern recognition,Medical imaging,Computer science,Visualization,Support vector machine,Image retrieval,Image processing,Artificial intelligence,Information gain ratio,Contextual image classification,High-resolution computed tomography | Conference |
ISSN | Citations | PageRank |
2372-9198 | 7 | 0.51 |
References | Authors | |
14 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Adrien Depeursinge | 1 | 418 | 38.83 |
| Jimison Iavindrasana | 2 | 106 | 8.91 |
| Gilles Cohen | 3 | 170 | 10.76 |
| Alexandra Platon | 4 | 142 | 12.05 |
| Pierre-Alexandre Poletti | 5 | 102 | 9.07 |
| Henning Müller | 6 | 2538 | 218.89 |