Name
Abstract | ||
|---|---|---|
In this paper, we present an automatic method to segment the chest wall in automated 3D breast ultrasound images. Determining the location of the chest wall in automated 3D breast ultrasound images is necessary in computer-aided detection systems to remove automatically detected cancer candidates beyond the chest wall and it can be of great help for inter- and intra-modal image registration. We show that the visible part of the chest wall in an automated 3D breast ultrasound image can be accurately modeled by a cylinder. We fit the surface of our cylinder model to a set of automatically detected rib-surface points. The detection of the rib-surface points is done by a classifier using features representing local image intensity patterns and presence of rib shadows. Due to attenuation of the ultrasound signal, a clear shadow is visible behind the ribs. Evaluation of our segmentation method is done by computing the distance of manually annotated rib points to the surface of the automatically detected chest wall. We examined the performance on images obtained with the two most common 3D breast ultrasound devices in the market. In a dataset of 142 images, the average mean distance of the annotated points to the segmented chest wall was 5.59±3.08mm. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1016/j.media.2012.11.005 | Medical Image Analysis |
Keywords | Field | DocType |
Chest wall segmentation,Automated 3D breast ultrasound,Cylinder fitting | Breast ultrasound,Computer vision,Pattern recognition,Rib cage,Segmentation,Artificial intelligence,Mathematics,Image registration,Ultrasound | Journal |
Volume | Issue | ISSN |
17 | 8 | 1361-8415 |
Citations | PageRank | References |
4 | 0.43 | 7 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tao Tan | 1 | 46 | 10.25 |
| Bram Platel | 2 | 245 | 21.42 |
| Ritse M Mann | 3 | 23 | 5.15 |
| Henkjan J. Huisman | 4 | 130 | 15.50 |
| Nico Karssemeijer | 5 | 992 | 122.49 |