Name
Abstract | ||
|---|---|---|
Expert radiologists are able to quickly detect atypical features in chest radiographs because they have developed a sense of what textures and contours are typical for each anatomic region by viewing a large set of “normal” chest radiographs. Our previous work modeled this type of learning with a transductive One-Nearest-Neighbor (1NN) method that was effective for identifying atypical regions in chest radiographs. However, the need to compute distances between the feature vectors extracted from a test image and a very large archive of feature vectors (previously extracted from corresponding anatomical locations in a large set of “normal” chest radiographs) made the 1NN method very computationally intensive. This paper uses an instance selection mechanism based on an Extended Fuzzy C-Means (EFCM) clustering algorithm to reduce the magnitude of this computation. Our results (based on a large set of real-world chest radiographs obtained from Mayo Clinic) indicate that EFCM can substantially reduce the computational cost of the 1NN method, without a substantial drop in the accuracy of its atypicality estimates. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/ISSPA.2012.6310544 | 2012 11th International Conference on Information Science, Signal Processing and their Applications (ISSPA) |
Keywords | Field | DocType |
Computer aided diagnosis,machine learning,biomedical imaging,X-rays | Computer vision,Object detection,Pattern recognition,Computer science,Feature (computer vision),Medical imaging,Anatomic region,Computer-aided diagnosis,Fuzzy set,Feature extraction,Artificial intelligence,Radiography | Conference |
ISBN | Citations | PageRank |
978-1-4673-0381-1 | 0 | 0.34 |
References | Authors | |
12 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mohammad Alzubaidi | 1 | 2 | 3.75 |
| Vineeth Nallure Balasubramanian | 2 | 265 | 36.44 |
| Ameet Patel | 3 | 0 | 1.01 |
| Sethuraman Panchanathan | 4 | 1431 | 152.04 |
| John A. Black Jr. | 5 | 48 | 3.59 |