Research Article Advanced Image Enhancement Method for Distant Vessels and Structures in Capsule Endoscopy Olivier Rukundo, 1 Marius Pedersen, 1 and Øistein Hovde 2 1 Department of Computer Science, Norwegian University of Science and Technology, Trondheim, Norway 2 Department of Gastroenterology, Innlandet Hospital Trust, 2819 Gjøvik, Norway Correspondence should be addressed to Olivier Rukundo; orukundo@gmail.com Received 10 July 2017; Revised 21 September 2017; Accepted 9 October 2017; Published 31 October 2017 Academic Editor: Kaan Yetilmezsoy Copyright © 2017 Olivier Rukundo et al. Tis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Tis paper proposes an advanced method for contrast enhancement of capsule endoscopic images, with the main objective to obtain sufcient information about the vessels and structures in more distant (or darker) parts of capsule endoscopic images. Te proposed method (PM) combines two algorithms for the enhancement of darker and brighter areas of capsule endoscopic images, respectively. Te half-unit weighted-bilinear algorithm (HWB) proposed in our previous work is used to enhance darker areas according to the darker map content of its HSV’s component . Enhancement of brighter areas is achieved thanks to the novel threshold weighted-bilinear algorithm (TWB) developed to avoid overexposure and enlargement of specular highlight spots while preserving the hue, in such areas. Te TWB performs enhancement operations following a gradual increment of the brightness of the brighter map content of its HSV’s component . In other words, the TWB decreases its averaged weights as the intensity content of the component  increases. Extensive experimental demonstrations were conducted, and, based on evaluation of the reference and PM enhanced images, a gastroenterologist (Ø.H.) concluded that the PM enhanced images were the best ones based on the information about the vessels, contrast in the images, and the view or visibility of the structures in more distant parts of the capsule endoscopy images. 1. Introduction In the efort to obtain more information about the vessels and structures, particularly, in the darker or distant parts of capsule endoscopic images, the image contrast enhancement is the way to go. Tere exist several categories and subcat- egories of contrast enhancement methods in the literature, for example, the Histogram Equalization (HE), Adaptive HE (AHE), and Contrast-Limited AHE (CLAHE) whose details are provided in [1, 2] as well as M1 a method proposed in [3], and M2 represents an enhancement method proposed in [4], developed to deal generally with the poor con- trast problems in color and grayscale images, which remain nonexhaustive and image dependent in their performance [5–12]. Today, capsule endoscopy is among the newest research and application areas in medicine that caught interest of many researchers because of the advantages that capsule endoscopy provides over the traditional endoscopy in terms of comforting patients while exploring the entire gastrointestinal (GI) tract [3, 13–16]. To clinically beneft from images obtained thanks to the capsule endoscope (CE), it is important to develop an advanced method that would deal carefully with the poor contrast problem caused generally by poor visibility conditions of the GI tract [17]. In this regard, a novel method using exclusively the bilinear interpolation algorithm has been proposed in [4] to deal with (1) the creation of artefacts leading to unnatural colors of the Histogram Equalization (HE) based methods without the need for converting Red-Blue-Green (RGB) to another color space [9, 10] and (2) the disadvantages of the generalized overexposure problem of the method proposed previously in [3]. Although experimental demonstrations showed that the half-unit weighted-bilinear algorithm (HWB), proposed in [4], made considerable improvements over the method proposed in [3], (improvements that can also be seen/noticed in this paper Figures 5(a)–11(a)), a gastroenterologist (ØH) Hindawi Computational and Mathematical Methods in Medicine Volume 2017, Article ID 9813165, 13 pages https://doi.org/10.1155/2017/9813165