NDLI: Multi-level local feature classification for bleeding detection in Wireless Capsule Endoscopy images

Content Provider	IEEE Xplore Digital Library
Author	Chee Khun Poh That Mon Htwe Liyuan Li Weijia Shen Jiang Liu Joo Hwee Lim Kap Luk Chan Ping Chun Tan
Copyright Year	2010
Description	Author affiliation: Computer Vision & Image Understanding Department, Institute for Infocomm Research (I2R), Agency for Science, Technology and Research (A*STAR) Singapore (Chee Khun Poh; That Mon Htwe; Liyuan Li; Weijia Shen; Jiang Liu; Joo Hwee Lim) \|\| School of Electrical & Electronics Engineering, Nanyang Technological University (NTU), Singapore (Kap Luk Chan; Ping Chun Tan)
Abstract	This paper presents a novel multi-level approach for bleeding detection in Wireless Capsule Endoscopy (WCE) images. In the low-level processing, each cell of K×K pixels is characterized by an adaptive color histogram which optimizes the information representation for WCE images. A Neural Network (NN) cell-classifier is trained to classify cells in an image as bleeding or non-bleeding patches. In the intermediate-level processing, a block which covers 3×3 cells is formed. The intermediate-level representation of the block is generated from the low-level classifications of the cells, which captures the spatial local correlations of the cell classifications. Again, a NN block-classifier is trained to classify the blocks as bleeding or non-bleeding ones. In the high-level processing, the low-level cell-based and intermediate-level block-based classifications are fused for final detection. In this way, our approach can combine the low-level features from pixels and intermediate-level features from local regions to achieve robust bleeding detection. Experiments on real WCE videos have shown that the proposed method of multi-level classification is not only accurate in both detection and localization of potential bleedings in WCE images but also robust to complex local noisy features.
Starting Page	76
Ending Page	81
File Size	648780
Page Count	6
File Format	PDF
ISBN	9781424464999
e-ISBN	9781424465026
DOI	10.1109/ICCIS.2010.5518576
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-06-28
Publisher Place	Singapore
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Hemorrhaging Endoscopes Neural networks Videos Histograms Computer vision Paper technology Color Robustness Biomedical imaging Neural Network (NN) and machine-learning Wireless Capsule Endoscopy (WCE) bleeding detection feature extraction adaptive color histogram block classification
Content Type	Text
Resource Type	Article

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