Name
Title | ||
|---|---|---|
Example-Based Assisting Approach for Pulmonary Nodule Classification in 3-D Thoracic CT Images |
Abstract | ||
|---|---|---|
This paper describes an example-based assisting approach for classifying pulmonary nodules in 3-D thoracic CT images. In this approach the internal and surrounding structures of the nodule are characterized by the distribution pattern of CT density and 3-D curvature indexes. Each nodule is represented by means of a joint histogram using the distance value fron the nodule center. When given an indeterminate nodule image, the images of lesions with known diagnoses (e.g. malignant va. benign) are retrieved from a 3-D nodule image database. The malignant likelihood of the indeterminate case is estimated by the difference between the representation pattern of the indeterminate case and the retrieved lesions. In the present study, we adopt the Mahalanobis distance as the difference measure and then, explore the feasibility of the classification based on pattern of similar lesion images. |
Year | DOI | Venue |
|---|---|---|
2002 | 10.1007/3-540-45786-0_98 | MICCAI |
Keywords | Field | DocType |
3-d thoracic ct image,3-d thoracic ct images,indeterminate case,3-d curvature index,pulmonary nodule classification,nodule center,distribution pattern,example-based assisting approach,3-d nodule image database,pulmonary nodule,ct density,indeterminate nodule image,representation pattern,indexation,mahalanobis distance | Computer vision,Histogram,Pattern recognition,Computer science,Mahalanobis distance,Artificial intelligence,Indeterminate,Image database,Medical diagnosis | Conference |
Volume | ISSN | ISBN |
2488 | 0302-9743 | 3-540-44224-3 |
Citations | PageRank | References |
0 | 0.34 | 4 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yoshiki Kawata | 1 | 192 | 54.44 |
| Noboru Niki | 2 | 188 | 66.10 |
| Hironobu Ohmatsu | 3 | 138 | 45.23 |
| Noriyuki Moriyama | 4 | 148 | 50.47 |