Abstract
Colonoscopy is a popular procedure which is used to detect an abnormality. Early diagnosis can help to heal many patients. The purpose of this paper is removing/reducing some artifacts to improve the visual quality of colonoscopy videos to provide better information for physicians. This work complements a series of work consisting of three previously published papers. In this paper, optic flow is used for motion compensation, where a number of consecutive images are registered to integrate some information to create a new image that has/reveals more information than the original one. Colon images were classified into informative and noninformative images by using a deep neural network. Then, two different strategies were used to treat informative and noninformative images. Informative images were treated by using Lucas Kanade with an adaptive temporal mean/median filter, whereas noninformative images were treated by using Lucas Kanade with a derivative of Gaussian (LKDOG) and adaptive temporal median images. Comparison showed that this work achieved better results than those achieved by the state-of-the-art strategies for the same degraded colon images data set. The new proposed algorithm reduced the error alignment by a factor of about 0.3, with a 100% successful image alignment ratio. In conclusion, this algorithm achieved better results than the state-of-the-art approaches in case of enhancing the informative images as shown in the results section; also, it helped to reveal some information from noninformative images that have very few details/no details.
Highlights
A series of our work, consisting of three previously published papers, described denoising different types of distorted colon images
The results showed that Lucas Kanade with a derivative of Gaussian (LKDOG) helped to remove large specular highlight areas in the noninformative images and converted them to informative images
Some experiments were conducted by using Lucas and Kanade (LK) optical flow with the temporal filter to treat individual or small areas of specular highlights that exist in the informative images
Summary
A series of our work, consisting of three previously published papers, described denoising different types of distorted colon images. The specular highlights are relatively small, and they move around in the image as the camera is moved In this case, specular highlights may be removed by image processing techniques that combine information from adjacent images in the colonoscopy video. Our objective is to enhance the visual quality of colonoscopy images by removing the specular highlights. A new motion compensation-based spatial temporal filter is proposed to enhance the quality of colonoscopy images. In this approach, specular highlights can be removed to be able to see visually important image features even in highly distorted images.
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