Name
Abstract | ||
|---|---|---|
Effective capability to search biomedical articles based on visual properties of article images may significantly augment information retrieval in the future. In this paper, we present a new method to classify the window setting types of brain CT images. Windowing is a technique frequently used in the evaluation of CT scans, and is used to enhance contrast for the particular tissue or abnormality type being evaluated. In particular, it provides radiologists with an enhanced view of certain types of cranial abnormalities, such as the skull lesions and bone dysplasia which are usually examined using the " bone window" setting and illustrated in biomedical articles using "bone window images". Due to the inherent large variations of images among articles, it is important that the proposed method is robust. Our algorithm attained 90% accuracy in classifying images as bone window or non-bone window in a 210 image data set. |
Year | Venue | Keywords |
|---|---|---|
2012 | AMIA | algorithms |
Field | DocType | Volume |
Pattern recognition,Abnormality,Dysplasia,Artificial intelligence,Medical physics,Skull,Medicine | Conference | 2012 |
ISSN | Citations | PageRank |
1942-597X | 0 | 0.34 |
References | Authors | |
4 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhiyun Xue | 1 | 245 | 22.97 |
| Sameer Antani | 2 | 1402 | 134.03 |
| L. Rodney Long | 3 | 534 | 56.98 |
| Dina Demner Fushman | 4 | 1717 | 147.70 |
| George R. Thoma | 5 | 1207 | 132.81 |