Publication: Bleeding Region Segmentation in Wireless Capsule Endoscopy Images by K-Mean Clustering Technique
1
0
Issued Date
2023
Resource Type
File Type
application/pdf
Rights Holder(s)
Scopus
Bibliographic Citation
2023 3rd International Symposium on Instrumentation, Control, Artificial Intelligence, and Robotics, ICA-SYMP 2023. Vol , No. (2023), p.69-72
Suggested Citation
Seebutda A., Sakuncharoenchaiya S., Numpacharoen K., Wiwatwattana N., Charoen A., Charoenpong T. Bleeding Region Segmentation in Wireless Capsule Endoscopy Images by K-Mean Clustering Technique. 2023 3rd International Symposium on Instrumentation, Control, Artificial Intelligence, and Robotics, ICA-SYMP 2023. Vol , No. (2023), p.69-72. doi:10.1109/ICA-SYMP56348.2023.10044741 Retrieved from: https://hdl.handle.net/20.500.14740/10737
Other Contributor(s)
Abstract
Wireless capsule endoscopy (WCE) is used to record internal images of the gastrointestinal tract. A common symptom such as gastrointestinal bleeding can be diagnosed by images. In this paper, we proposed a method for bleeding region in gastrointestinal segmentation by the K-Mean Clustering technique. The images were captured by wireless capsule endoscopy (WCE). This method consists of three steps: preprocessing, color clustering, and bleeding region segmentation. Firstly, input data in RGB color space is converted to L*a*b∗ color space. Color intensity has two cluster which is bleeding region, and background. The K-Mean technique is used to group the data. Finally, bleeding region is defined by intensity in red layer. In experimental result, 48 images from KID Atlas dataset are used. The accuracy rate is 84.26%, DICE rate is 67.71%, Jaccard Index (JI) is 60.43%, sensitivity rate is 69.84% and the precision rate is 65.70%. The results is satisfactory for future improvement. © 2023 IEEE.