Adaptive Specular Reflection Detection and Inpainting in Colonoscopy Video Frames

Akbari, Mojtaba; Mohrekesh, Majid; Najariani, Kayvan; Karimi, Nader; Samavi, Shadrokh; Soroushmehr, S. M. Reza

doi:10.1109/icip.2018.8451699

Cited by 19 publications

(6 citation statements)

References 10 publications

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“…One study developed an inpainting method for endoscopy medical images which was also able to remove specular highlights of polyps [10]. Akbari et al [11] removed reflections in colonoscopy video frames using a proposed inpainting method. A GAN has also been developed to do inpainting reflections in endoscopic images [12].…”

Section: Related Workmentioning

confidence: 99%

PolypConnect: Image inpainting for generating realistic gastrointestinal tract images with polyps

Fagereng¹,

Thambawita²,

Storås³

et al. 2022

Preprint

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Early identification of a polyp in the lower gastrointestinal (GI) tract can lead to prevention of life-threatening colorectal cancer. Developing computer-aided diagnosis (CAD) systems to detect polyps can improve detection accuracy and efficiency and save the time of the domain experts called endoscopists. Lack of annotated data is a common challenge when building CAD systems. Generating synthetic medical data is an active research area to overcome the problem of having relatively few true positive cases in the medical domain. To be able to efficiently train machine learning (ML) models, which are the core of CAD systems, a considerable amount of data should be used. In this respect, we propose the PolypConnect pipeline, which can convert non-polyp images into polyp images to increase the size of training datasets for training. We present the whole pipeline with quantitative and qualitative evaluations involving endoscopists. The polyp segmentation model trained using synthetic data, and real data shows a 5.1% improvement of mean intersection over union (mIOU), compared to the model trained only using real data. The codes of all the experiments are available on GitHub to reproduce the results.

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Section: Related Workmentioning

confidence: 99%

PolypConnect: Image inpainting for generating realistic gastrointestinal tract images with polyps

Fagereng¹,

Thambawita²,

Storås³

et al. 2022

Preprint

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show abstract

“…This separation helps in detecting and classifying the specular and non‐specular reflection regions [67]. Akbari et al [68] state that the specular reflections exhibit very low colour saturation values and therefore, threshold and input colonoscopy image with respect to saturation values for specularity detection. When we transform facial images with specular reflections into the HSV colour domain, we noticed that not only specular reflection (very bright) pixels but also very dark pixels exhibit very low colour saturation.…”

Section: Spectacle Problemmentioning

confidence: 99%

Human video database for facial feature detection under spectacles with varying alertness levels: a baseline study

Gupta

Mayaluri

Panda

2020

Cognitive Computation and Systems

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“…In addition, through vaginal mirror screening for cervical cancer, specular reflection may change the appearance of the tissue and interfere with the performance of the segmentation of the cervical lesions area algorithm [ 5 , 6 , 7 ]. In colonoscopy imaging research, the detection of polyps and colorectal cancer may be affected by specular reflection [ 8 , 9 ]. Laryngeal cancer is detected by the laryngeal mirror, and the interference caused by specular reflection may cause the correct classification of laryngeal cancer tissue to fail [ 10 ].…”

Section: Introductionmentioning

confidence: 99%

“…the highlights can no longer be considered sparse parts, and the method does not work well. Image inpainting-based methods [ 6 , 7 , 8 , 19 , 27 , 28 , 29 , 30 , 31 ] first detect the highlighted regions in the image and then inpaint with the most similar parts in the image. It can restore the specular regions under certain conditions, but it may be unreasonable for the reconstruction of human tissue structure, or the recovery effect for large-area specular regions is not good, or the algorithm operation efficiency is low.…”

Section: Introductionmentioning

confidence: 99%

Specular Reflections Detection and Removal for Endoscopic Images Based on Brightness Classification

Nie

Chen

et al. 2023

Sensors

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Specular Reflections often exist in the endoscopic image, which not only hurts many computer vision algorithms but also seriously interferes with the observation and judgment of the surgeon. The information behind the recovery specular reflection areas is a necessary pre-processing step in medical image analysis and application. The existing highlight detection method is usually only suitable for medium-brightness images. The existing highlight removal method is only applicable to images without large specular regions, when dealing with high-resolution medical images with complex texture information, not only does it have a poor recovery effect, but the algorithm operation efficiency is also low. To overcome these limitations, this paper proposes a specular reflection detection and removal method for endoscopic images based on brightness classification. It can effectively detect the specular regions in endoscopic images of different brightness and can improve the operating efficiency of the algorithm while restoring the texture structure information of the high-resolution image. In addition to achieving image brightness classification and enhancing the brightness component of low-brightness images, this method also includes two new steps: In the highlight detection phase, the adaptive threshold function that changes with the brightness of the image is used to detect absolute highlights. During the highlight recovery phase, the priority function of the exemplar-based image inpainting algorithm was modified to ensure reasonable and correct repairs. At the same time, local priority computing and adaptive local search strategies were used to improve algorithm efficiency and reduce error matching. The experimental results show that compared with the other state-of-the-art, our method shows better performance in terms of qualitative and quantitative evaluations, and the algorithm efficiency is greatly improved when processing high-resolution endoscopy images.

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Adaptive Specular Reflection Detection and Inpainting in Colonoscopy Video Frames

Cited by 19 publications

References 10 publications

PolypConnect: Image inpainting for generating realistic gastrointestinal tract images with polyps

PolypConnect: Image inpainting for generating realistic gastrointestinal tract images with polyps

Human video database for facial feature detection under spectacles with varying alertness levels: a baseline study

Specular Reflections Detection and Removal for Endoscopic Images Based on Brightness Classification

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