Name
Title | ||
|---|---|---|
Computer vision approach to detect colonic polyps in computed tomographic colonography |
Abstract | ||
|---|---|---|
In this paper, we present evaluation results for a novel colonic polyp classification method for use as part of a computed tomographic colonography (CTC) computer-aided detection (CAD) algorithm. Inspired by the interpretative methodology of radiologists using 3D fly-through mode in CTC reading, we have developed an algorithm which utilizes sequences of images (referred to here as videos) for classification of CAD marks. First, we generated an initial list of polyp candidates using an existing CAD system. For each of these candidates, we created a video composed of a series of intraluminal, volume-rendered images focusing on the candidate from multiple viewpoints. These videos illustrated the shape of the polyp candidate and gathered contextual information of diagnostic importance. We calculated the histogram of oriented gradients (HOG) feature on each frame of the video and utilized a support vector machine for classification. We tested our method by analyzing a CTC data set of 50 patients from three medical centers. Our proposed video analysis method for polyp classification showed significantly better performance than an approach using only the 2D CT slice data. The areas under the ROC curve for these methods were 0.88 (95% CI: [0.84, 0.91]) and 0.80 (95% CI: [0.75, 0.84]) respectively (p=0.0005). |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1117/12.911657 | Proceedings of SPIE |
Keywords | Field | DocType |
Computed tomographic colonography,video analysis,computer-aided detection | CAD,Computer vision,Contextual information,Computer science,Computer-aided diagnosis,Support vector machine,Histogram of oriented gradients,Computed Tomographic Colonography,Artificial intelligence,Virtual colonoscopy,Cad system | Conference |
Volume | ISSN | Citations |
8315 | 0277-786X | 0 |
PageRank | References | Authors |
0.34 | 0 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Matthew McKenna | 1 | 19 | 2.70 |
| Shijun Wang | 2 | 239 | 22.83 |
| Tan Nguyen | 3 | 61 | 5.22 |
| Joseph E. Burns | 4 | 89 | 9.51 |
| Nicholas Petrick | 5 | 209 | 42.63 |
| Berkman Sahiner | 6 | 224 | 66.72 |
| Ronald M. Summers | 7 | 893 | 86.16 |