Title :
Helicobacter pylori infection detection from gastric X-ray images using KLFDA-based decision fusion
Author :
Kenta Ishihara;Takahiro Ogawa;Miki Haseyama
Author_Institution :
Graduate School of Information Science and Technology, Hokkaido University, N-14, W-9, Kita-ku, Sapporo, Hokkaido, 060-0814, Japan
Abstract :
This paper presents the performance improvement of Helicobacter pylori (H. pylori) infection detection using Kernel Local Fisher Discriminant Analysis (KLFDA)-based decision fusion. As the biggest contribution of this paper, the proposed method extracts more discriminative features based on KLFDA for the decision fusion. Since the decision fusion employed in this paper can consider not only the detection results but also the visual features, by calculating more discriminative features via KLFDA, more accurate decision fusion becomes feasible. Furthermore, experimental results show the effectiveness of the proposed method.
Keywords :
"Feature extraction","X-ray imaging","Cancer","Kernel","Endoscopes","Sensitivity","Biomedical imaging"
Conference_Titel :
Consumer Electronics (GCCE), 2015 IEEE 4th Global Conference on
DOI :
10.1109/GCCE.2015.7398563