A frame reduction system based on a color structural similarity (CSS) method and Bayer images analysis for capsule endoscopy

Qasim Al-Shebani, Prashan Premaratne, Darryl J McAndrew, Peter J Vial, Shehan Abey
Artificial Intelligence in Medicine 2019, 94: 18-27
A capsule endoscopy examination of the human small bowel generates a large number of images that have high similarity. In order to reduce the time it takes to review the high similarity images, clinicians will increase the playback speed, typically to 15 frames per second [1]. Associated with this behaviour is an increased probability of overlooking an image that may contain an abnormality. An alternative option to increasing the playback speed is the application of abnormality detection systems to detect abnormalities such as ulcers, tumors, polyps and bleeding. However, applying all of these detection systems requires significant computing time and still produces numerous images with high similarity depending on the specificity of the utilized detection systems. An interesting approach to reduce viewing time is the application of a frame reduction system that reduces the number of images by omitting those with a high similarity of information. The advantage of such a system is that the specialist only needs to review a single image that technically represents a series of images with high similarity. This reduces the total number of images that a specialist must review and importantly, images containing any abnormality are not removed from the review, but simply reduced in number. Thus, the current study developed a frame reduction system using various color models using Bayer images for color texture and a modified local binary pattern (LBP) for structural information. The proposed system achieved a reduction ratio of 93.87%, which is higher than the existing systems and required lesser computation due to the utilization of Bayer images.


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