Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6920753 | Computers in Biology and Medicine | 2016 | 7 Pages |
Abstract
Since technical knowledge and a high degree of experience are necessary for diagnosis of chronic gastritis, computer-aided diagnosis (CAD) systems that analyze gastric X-ray images are desirable in the field of medicine. Therefore, a new method that estimates salient regions related to chronic gastritis/non-gastritis for supporting diagnosis is presented in this paper. In order to estimate salient regions related to chronic gastritis/non-gastritis, the proposed method monitors the distance between a target image feature and Support Vector Machine (SVM)-based hyperplane for its classification. Furthermore, our method realizes removal of the influence of regions outside the stomach by using positional relationships between the stomach and other organs. Consequently, since the proposed method successfully estimates salient regions of gastric X-ray images for which chronic gastritis and non-gastritis are unknown, visual support for inexperienced clinicians becomes feasible.
Keywords
Related Topics
Physical Sciences and Engineering
Computer Science
Computer Science Applications
Authors
Ren Togo, Kenta Ishihara, Takahiro Ogawa, Miki Haseyama,