Name
Title | ||
|---|---|---|
Combining Statistical And Geometric Features For Colonic Polyp Detection In Ctc Based On Multiple Kernel Learning |
Abstract | ||
|---|---|---|
Computed tomographic colonography (CTC) combined with a computer aided detection system provides a feasible approach for improving colonic polyps detection and increasing the use of CTC for colon cancer screening. To distinguish true polyps from false positives, various features extracted from polyp candidates have been proposed. Most of these traditional features try to capture the shape information of polyp candidates or neighborhood knowledge about the surrounding structures (fold, colon wall, etc.). In this paper, we propose a new set of shape descriptors for polyp candidates based on statistical curvature information. These features called histograms of curvature features are rotation, translation and scale invariant and can be treated as complementing existing feature set. Then in order to make full use of the traditional geometric features (defined as group A) and the new statistical features (group B) which are highly heterogeneous, we employed a multiple kernel learning method based on semi-definite programming to learn an optimized classification kernel from the two groups of features. We conducted leave-one-patient-out test on a CTC dataset which contained scans from 66 patients. Experimental results show that a support vector machine (SVM) based on the combined feature set and the semi-definite optimization kernel achieved higher FROC performance compared to SVMs using the two groups of features separately. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1142/S1469026810002744 | INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE AND APPLICATIONS |
Keywords | Field | DocType |
Computed tomographic colonography, colonic polyp detection, multiple kernel learning, histograms of curvature features, semi-definite programming | Histogram,Computer science,Feature set,Artificial intelligence,Computed Tomographic Colonography,Colon wall,Computer vision,Curvature,Pattern recognition,Multiple kernel learning,Colonic Polyp,Machine learning,False positive paradox | Journal |
Volume | Issue | ISSN |
9 | 1 | 1469-0268 |
Citations | PageRank | References |
9 | 0.76 | 10 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shijun Wang | 1 | 239 | 22.83 |
| Jianhua Yao | 2 | 1135 | 110.49 |
| Nicholas Petrick | 3 | 209 | 42.63 |
| Ronald M Summers | 4 | 13 | 1.17 |