Preprint Article Version 2 This version is not peer-reviewed

Stochastic Capsule Endoscopy Image Enhancement

Version 1 : Received: 25 April 2018 / Approved: 26 April 2018 / Online: 26 April 2018 (08:01:31 CEST)
Version 2 : Received: 16 May 2018 / Approved: 17 May 2018 / Online: 17 May 2018 (12:46:30 CEST)

A peer-reviewed article of this Preprint also exists.

Mohammed, A.; Farup, I.; Pedersen, M.; Hovde, Ø.; Yildirim Yayilgan, S. Stochastic Capsule Endoscopy Image Enhancement. J. Imaging 2018, 4, 75. Mohammed, A.; Farup, I.; Pedersen, M.; Hovde, Ø.; Yildirim Yayilgan, S. Stochastic Capsule Endoscopy Image Enhancement. J. Imaging 2018, 4, 75.

Journal reference: J. Imaging 2018, 4, 75
DOI: 10.3390/jimaging4060075

Abstract

Capsule endoscopy, which uses a wireless camera to take images of the digestive tract, is emerging as an alternative to traditional colonoscopy. The diagnostic values of these images depend on the quality of revealed underlying tissue surfaces. In this paper, we consider the problem of enhancing the visibility of detail and shadowed tissue surfaces for capsule endoscopy images. Using concentric circles at each pixel for random walks combined with stochastic sampling, the proposed method enhances the details of vessel and tissue surfaces. The framework decomposes the image into two detail layers that contain shadowed tissue surfaces and detail features. The target pixel value is recalculated for the smooth layer using similarity of the target pixel to neighboring pixels by weighting against the total gradient variation and intensity differences. In order to evaluate the diagnostic image quality of the proposed method, we used clinical subjective evaluation with a rank order on selected KID image database and compared to state of the art enhancement methods. The result showed that the proposed method provides a better result in terms of diagnostic image quality and objective quality contrast metrics and structural similarity index.

Subject Areas

capsule video endoscopy; stochastic sampling; random walks; color gradient; image decomposition

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