Helicobacter pylori infection detection from multiple X-ray images based on decision level fusion

This paper presents an automatic detection method of Helicobacter pylori (H. pylori) infection from multiple gastric X-ray images. As the biggest contribution of this paper, we combine multiple detection results based on a decision level fusion. In order to obtain multiple detection results, the proposed method first focuses on characteristics of gastric X-ray images with H. pylori infection and computes several visual features from multiple X-ray images taken in several specific directions. Second, we select effective features for H. pylori infection detection from all features based on the minimal-Redundancy-Maximal-Relevance algorithm, and the selected features are used to train the Support Vector Machine (SVM) classifiers that are constructed for each direction of gastric radiography. Therefore, the detection of H. pylori infection becomes feasible, and we can obtain multiple detection results from the SVM classifiers. Furthermore, we combine multiple detection results based on the decision level fusion scheme considering the detection performance of each SVM classifier. Experimental results obtained by applying the proposed method to real X-ray images prove the effectiveness of the proposed method.