Informative frame detection from wireless capsule video endoscopic images

Md. Khayrul Bashar; Kensaku Mori; Yasuhito Suenaga; Takayuki Kitasaka; Yoshito Mekada

doi:10.1117/12.772418

11 March 2008 Informative frame detection from wireless capsule video endoscopic images

Md. Khayrul Bashar, Kensaku Mori, Yasuhito Suenaga, Takayuki Kitasaka, Yoshito Mekada

Proceedings Volume 6914, Medical Imaging 2008: Image Processing; 69142A (2008) https://doi.org/10.1117/12.772418
Event: Medical Imaging, 2008, San Diego, California, United States

Abstract

Wireless capsule endoscopy (WCE) is a new clinical technology permitting the visualization of the small bowel, the most difficult segment of the digestive tract. The major drawback of this technology is the high amount of time for video diagnosis. In this study, we propose a method for informative frame detection by isolating useless frames that are substantially covered by turbid fluids or their contamination with other materials, e.g., faecal, semi-processed or unabsorbed foods etc. Such materials and fluids present a wide range of colors, from brown to yellow, and/or bubble-like texture patterns. The detection scheme, therefore, consists of two stages: highly contaminated non-bubbled (HCN) frame detection and significantly bubbled (SB) frame detection. Local color moments in the Ohta color space are used to characterize HCN frames, which are isolated by the Support Vector Machine (SVM) classifier in Stage-1. The rest of the frames go to the Stage-2, where Laguerre gauss Circular Harmonic Functions (LG-CHFs) extract the characteristics of the bubble-structures in a multi-resolution framework. An automatic segmentation method is designed to extract the bubbled regions based on local absolute energies of the CHF responses, derived from the grayscale version of the original color image. Final detection of the informative frames is obtained by using threshold operation on the extracted regions. An experiment with 20,558 frames from the three videos shows the excellent average detection accuracy (96.75%) by the proposed method, when compared with the Gabor based- (74.29%) and discrete wavelet based features (62.21%).

Citation Download Citation

Md. Khayrul Bashar, Kensaku Mori, Yasuhito Suenaga, Takayuki Kitasaka, and Yoshito Mekada "Informative frame detection from wireless capsule video endoscopic images", Proc. SPIE 6914, Medical Imaging 2008: Image Processing, 69142A (11 March 2008); https://doi.org/10.1117/12.772418

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KEYWORDS

Image segmentation

Video

Endoscopy

Image filtering

Wavelets

Visualization

Tissues

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