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Cluster based statistical feature extraction method for automatic bleeding detection in wireless capsule endoscopy video.

Author(s): Ghosh T, Fattah SA, Wahid KA, Zhu WP, Ahmad MO

Comput Biol Med. 2018 03 01;94:41-54 Authors: Ghosh T, Fattah SA, Wahid KA, Zhu WP, Ahmad MO

Article GUID: 29407997
Mass detection in digital breast tomosynthesis data using convolutional neural networks and multiple instance learning.

Author(s): Yousefi M, Krzyzak A, Suen CY

Comput Biol Med. 2018 05 01;96:283-293 Authors: Yousefi M, Krzyżak A, Suen CY

Article GUID: 29665537
Title:Cluster based statistical feature extraction method for automatic bleeding detection in wireless capsule endoscopy video.
Authors:Ghosh T,  Fattah SA,  Wahid KA,  Zhu WP,  Ahmad MO
Link:https://www.ncbi.nlm.nih.gov/pubmed/29407997?dopt=Abstract
DOI:10.1016/j.compbiomed.2017.12.014
Category:Comput Biol Med
PMID:29407997
Dept Affiliation: IMAGING
1 Dept. of Electrical and Electronic Engineering, Bangladesh University of Engineering and Technology, Dhaka, Bangladesh.
2 Dept. of Electrical and Electronic Engineering, Bangladesh University of Engineering and Technology, Dhaka, Bangladesh. Electronic address: fattah@eee.buet.ac.bd.
3 Dept. of Electrical and Computer Engineering, University of Saskatchewan, Saskatoon, Canada.
4 Dept. of Electrical and Computer Engineering, Concordia University, Montreal, Canada.

Description:

Cluster based statistical feature extraction method for automatic bleeding detection in wireless capsule endoscopy video.

Comput Biol Med. 2018 03 01;94:41-54

Authors: Ghosh T, Fattah SA, Wahid KA, Zhu WP, Ahmad MO

Abstract

Wireless capsule endoscopy (WCE) is capable of demonstrating the entire gastrointestinal tract at an expense of exhaustive reviewing process for detecting bleeding disorders. The main objective is to develop an automatic method for identifying the bleeding frames and zones from WCE video. Different statistical features are extracted from the overlapping spatial blocks of the preprocessed WCE image in a transformed color plane containing green to red pixel ratio. The unique idea of the proposed method is to first perform unsupervised clustering of different blocks for obtaining two clusters and then extract cluster based features (CBFs). Finally, a global feature consisting of the CBFs and differential CBF is used to detect bleeding frame via supervised classification. In order to handle continuous WCE video, a post-processing scheme is introduced utilizing the feature trends in neighboring frames. The CBF along with some morphological operations is employed to identify bleeding zones. Based on extensive experimentation on several WCE videos, it is found that the proposed method offers significantly better performance in comparison to some existing methods in terms of bleeding detection accuracy, sensitivity, specificity and precision in bleeding zone detection. It is found that the bleeding detection performance obtained by using the proposed CBF based global feature is better than the feature extracted from the non-clustered image. The proposed method can reduce the burden of physicians in investigating WCE video to detect bleeding frame and zone with a high level of accuracy.

PMID: 29407997 [PubMed - indexed for MEDLINE]