Name
Abstract | ||
|---|---|---|
Tumor size is an objective measure that is used to evaluate the effectiveness of anticancer agents. Responses to therapy are categorized as complete response, partial response, stable disease and progressive disease. Implicit in this scheme is the change in the tumor over time; however, most tumor segmentation algorithms do not use temporal information. Here we introduce an automated method using probabilistic reasoning over both space and time to segment brain tumors from 4D spatio-temporal MRI data. The 3D expectation-maximization method is extended using the hidden Markov model to infer tumor classification based on previous and subsequent segmentation results. Spatial coherence via a Markov Random Field was included in the 3D spatial model. Simulated images as well as patient images from three independent sources were used to validate this method. The sensitivity and specificity of tumor segmentation using this spatio-temporal model is improved over commonly used spatial or temporal models alone. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1016/j.cmpb.2006.09.007 | Computer Methods and Programs in Biomedicine |
Keywords | Field | DocType |
segment brain tumor,tumor segmentation,spatial coherence,spatial model,automated method,mr image,expectation-maximization method,tumor segmentation algorithm,tumor classification,hidden markov model,tumor size,probabilistic reasoning,expectation maximization | Computer vision,Scale-space segmentation,Pattern recognition,Segmentation,Markov random field,Expectation–maximization algorithm,Computer science,Brain tumor,Software,Artificial intelligence,Probabilistic logic,Hidden Markov model | Journal |
Volume | Issue | ISSN |
84 | 2-3 | 0169-2607 |
Citations | PageRank | References |
10 | 0.55 | 9 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jeffrey Solomon | 1 | 23 | 4.37 |
| John A. Butman | 2 | 52 | 11.18 |
| Arun Sood | 3 | 124 | 29.36 |