An algorithm for automated segmentation for bleeding detection in endoscopic images

Eva Tuba, Milan Tuba, Raka Jovanovic

Research output: Chapter in Book/Report/Conference proceedingConference contribution

19 Citations (Scopus)

Abstract

Wireless capsule endoscopy is an important advanced diagnostics method. It produces huge amount of images during travel through patient's digestive tract and that usually requires automated analysis. One of the most important abnormalities is bleeding and automated segmentation for bleeding detection is an active research topic. In this paper we propose an algorithm for automated segmentation for bleeding detection in capsule endoscopy images. The algorithm uses block based segmentation where average saturation from the HSI model and skewness and kurtosis of uniform local binary patterns histogram are used as features for the support vector machine classifier. Support vector machine parameters are tuned using grid search. The proposed method was tested using standard benchmark images and compared with other approaches from literature using Dice similarity coefficient and misclassification error as metrics, where it obtained better results using simpler features.

Original languageEnglish
Title of host publication2017 International Joint Conference on Neural Networks, IJCNN 2017 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages4579-4586
Number of pages8
Volume2017-May
ISBN (Electronic)9781509061815
DOIs
Publication statusPublished - 30 Jun 2017
Event2017 International Joint Conference on Neural Networks, IJCNN 2017 - Anchorage, United States
Duration: 14 May 201719 May 2017

Other

Other2017 International Joint Conference on Neural Networks, IJCNN 2017
CountryUnited States
CityAnchorage
Period14/5/1719/5/17

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ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

Cite this

Tuba, E., Tuba, M., & Jovanovic, R. (2017). An algorithm for automated segmentation for bleeding detection in endoscopic images. In 2017 International Joint Conference on Neural Networks, IJCNN 2017 - Proceedings (Vol. 2017-May, pp. 4579-4586). [7966437] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IJCNN.2017.7966437