Title :
A training based Support Vector Machine technique for blood detection in wireless capsule endoscopy images
Author :
Jie Li ; Jinwen Ma ; Tillo, Tammam ; Bailing Zhang ; Eng Gee Lim
Author_Institution :
Dept. of Electr. & Electron. Eng., Xi´an Jiaotong Liverpool Univ., Suzhou, China
Abstract :
Wireless capsule endoscopy (WCE) is a non-invasive technique which could detect variety of abnormalities in the small bowel. However, in many cases it is difficult for doctors to distinguish obscure gastrointestinal bleeding of patients; moreover, the diagnosis process of the obtained video could take long time due to the huge number of generated frames. This project provides a method to automatically detect bleeding areas in the WCE images by using Support Vector Machine (SVM) classifier as the main engine with learning based mechanism in order to increase the accuracy. The experiment results show that this could not only advance the accuracy of WCE diagnosis, but also reduce the diagnosis time effectively. To further improve the performance of the proposed approach the length of the learning-based database was also tuned.
Keywords :
biomedical transducers; blood; diseases; endoscopes; support vector machines; wireless sensor networks; SVM classifier; WCE images; blood detection; gastrointestinal bleeding; generated frames; learning based mechanism; learning-based database; small bowel; training based support vector machine; wireless capsule endoscopy images; Support Vector Machine; Wireless capsule endoscopy; bleeding detection; learning based mechanism;
Conference_Titel :
Biomedical Engineering and Sciences (IECBES), 2012 IEEE EMBS Conference on
Conference_Location :
Langkawi
Print_ISBN :
978-1-4673-1664-4
DOI :
10.1109/IECBES.2012.6498194