Name
Abstract | ||
|---|---|---|
This paper describes a system for detecting pulmonary nodules in CT images. It aims to label individual image voxels in accordance to one of a number of anatomical (pulmonary vessels or junctions), pathological (nodules), or spurious (noise) events. The approach is orthodoxly Bayesian, with particular care taken in the objective establishment of prior probabilities and the incorporation of relevant medical knowledge. We provide, under explicit modeling assumptions, closed-form expressions for all the probability distributions involved. The technique is applied to real data, and we present a discussion of its performance. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1007/978-3-540-73273-0_12 | IPMI |
Keywords | Field | DocType |
probability distribution | Voxel,Computer vision,Expression (mathematics),Pattern recognition,Lung,Computer science,Medical knowledge,Probability distribution,Artificial intelligence,Principle of maximum entropy,Spurious relationship,Bayesian probability | Conference |
Volume | ISSN | Citations |
20 | 0302-9743 | 9 |
PageRank | References | Authors |
0.88 | 15 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Paulo R. S. Mendonça | 1 | 610 | 50.38 |
| Rahul Bhotika | 2 | 142 | 17.94 |
| Fei Zhao | 3 | 9 | 0.88 |
| James V. Miller | 4 | 208 | 51.13 |