Name
Abstract | ||
|---|---|---|
In this paper we investigate a method for segmentation of colorectal Narrow Band Imaging (NBI) endoscopic images with Support Vector Machine (SVM) and Markov Random Field (MRF). SVM classifiers recognize each square patch of an NBI image and output posterior probabilities that represent how likely the given patch falls into a certain label. To prevent the spatial inconsistency between adjacent patches and encourage segmented regions to have smoother shapes, MRF is introduced by using the posterior outputs of SVMs as a unary term of MRF energy function. Segmentation results of 1191 NBI images are evaluated in experiments in which SVMs were trained with 480 trimmed NBI images and the MRF energy was minimized by an α - β swap Graph Cut. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/EMBC.2014.6944683 | EMBC |
Keywords | Field | DocType |
output posterior probability,endoscopes,colorectal narrow band imaging endoscopic images,random processes,α-β swap graph cut,biomedical optical imaging,image segmentation,markov processes,image classification,support vector machine,markov random field,graph theory,svm classifiers,svm-mrf segmentation,colorectal nbi endoscopic images,mrf energy function,support vector machines,medical image processing,probability | Computer vision,Scale-space segmentation,Pattern recognition,Computer science,Segmentation,Support vector machine,Segmentation-based object categorization,Image segmentation,Artificial intelligence | Conference |
Volume | ISSN | Citations |
2014 | 1557-170X | 1 |
PageRank | References | Authors |
0.35 | 9 | 8 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tsubasa Hirakawa | 1 | 1 | 0.35 |
| Tom Tamaki | 2 | 1 | 0.35 |
| Bisser Raytchev | 3 | 2 | 1.33 |
| Kazufumi Kaneda | 4 | 1 | 0.35 |
| Tetsushi Koide | 5 | 1 | 0.35 |
| Yoko Kominami | 6 | 2 | 0.70 |
| Shigeto Yoshida | 7 | 3 | 0.77 |
| Shinji Tanaka | 8 | 1 | 0.35 |