Stochastic Capsule Endoscopy Image Enhancement

Mohammed, Ahmed; Farup, Ivar; Pedersen, Marius; Hovde, Øistein; Yayilgan, Sule Yildirim

doi:10.20944/preprints201804.0333.v1

Cited by 6 publications

(9 citation statements)

References 26 publications

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“…One important issue is the high similarity among some cases in WCE frames with different lesions that complicates the detection process (see Figure 2). e quality of WCE images is lower than traditional endoscopy or colonoscopy images and also these images suffer from noise, low resolution, and blurriness [7].…”

Section: Introductionmentioning

confidence: 99%

A Computer-Aided Method for Digestive System Abnormality Detection in WCE Images

Amiri

Hassanpour

Beghdadi

2021

Journal of Healthcare Engineering

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Wireless capsule endoscopy (WCE) is a powerful tool for the diagnosis of gastrointestinal diseases. The output of this tool is in video with a length of about eight hours, containing about 8000 frames. It is a difficult task for a physician to review all of the video frames. In this paper, a new abnormality detection system for WCE images is proposed. The proposed system has four main steps: (1) preprocessing, (2) region of interest (ROI) extraction, (3) feature extraction, and (4) classification. In ROI extraction, at first, distinct areas are highlighted and nondistinct areas are faded by using the joint normal distribution; then, distinct areas are extracted as an ROI segment by considering a threshold. The main idea is to extract abnormal areas in each frame. Therefore, it can be used to extract various lesions in WCE images. In the feature extraction step, three different types of features (color, texture, and shape) are employed. Finally, the features are classified using the support vector machine. The proposed system was tested on the Kvasir-Capsule dataset. The proposed system can detect multiple lesions from WCE frames with high accuracy.

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Section: Introductionmentioning

confidence: 99%

A Computer-Aided Method for Digestive System Abnormality Detection in WCE Images

Amiri

Hassanpour

Beghdadi

2021

Journal of Healthcare Engineering

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“… Qualitative results of the enhancement methods: (a) Original image. (b)Image enhanced by stochastic enhancement method [25] . (c) Image enhanced by tone mapping.…”

Section: Methodsmentioning

confidence: 99%

“…One of the evaluated enhancement methods is the stochastic sampling-based image enhancement algorithm proposed by Mohammed et al [25] that helps in highlighting the lung tissues and the bronchioles in the CT images. The approach explores the local neighborhood of a pixel in two capacities.…”

Section: Methodsmentioning

confidence: 99%

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Weakly-Supervised Network for Detection of COVID-19 in Chest CT Scans

et al. 2020

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Deep Learning-based chest Computed Tomography (CT) analysis has been proven to be effective and efficient for COVID-19 diagnosis. Existing deep learning approaches heavily rely on large labeled data sets, which are difficult to acquire in this pandemic situation. Therefore, weakly-supervised approaches are in demand. In this paper, we propose an end-to-end weakly-supervised COVID-19 detection approach, ResNext+, that only requires volume level data labels and can provide slice level prediction. The proposed approach incorporates a lung segmentation mask as well as spatial and channel attention to extract spatial features. Besides, Long Short Term Memory (LSTM) is utilized to acquire the axial dependency of the slices. Moreover, a slice attention module is applied before the final fully connected layer to generate the slice level prediction without additional supervision. An ablation study is conducted to show the efficiency of the attention blocks and the segmentation mask block. Experimental results, obtained from publicly available datasets, show a precision of 81.9% and F1 score of 81.4%. The closest state-of-the-art gives 76.7% precision and 78.8% F1 score. The 5% improvement in precision and 3% in the F1 score demonstrate the effectiveness of the proposed method. It is worth noticing that, applying image enhancement approaches do not improve the performance of the proposed method, sometimes even harm the scores, although the enhanced images have better perceptual quality.

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“…Another very interesting and less investigated area of research, where the notion of contrast plays a prominent role is the objective evaluation of CE methods. One example is in medical CE [136], [137]. Sdiri et al [137] used among others the Absolute Measure of Enhancement (AME), which is based on the Weber-Fechner contrast (Eq.…”

Section: A Image Quality Assessment (Iqa)mentioning

confidence: 99%

A Critical Analysis on Perceptual Contrast and Its Use in Visual Information Analysis and Processing

Beghdadi¹,

Qureshi

Amirshahi

et al. 2020

IEEE Access

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Contrast in visible images is one of the most relevant characteristics of visual signals. Since the pioneering works performed in vision psychology and optics, different definitions have been proposed in the literature. However, for the time being there exist no definition of contrast on which the vision research and visual processing scientific community can agree on. This makes it critical to have a clear view on the notion of contrast and its use in various applications. One issue to consider is how to define and particularly use this important measure in developing image processing and analysis methods. In this paper, we present a critical review of contrast measures and associated models developed by the scientific community in vision, optics, and image processing. We also provide learned lessons and guidelines on the use of appropriate contrast measures in selected visual information processing and analysis applications. We discuss challenges and propose research avenues for models enriched by recent findings in the field of human vision research and machine learning. We believe that this work serves as a guideline and can potentially open new research perspectives to the scientific community working on visual information processing and analysis.

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Stochastic Capsule Endoscopy Image Enhancement

Cited by 6 publications

References 26 publications

A Computer-Aided Method for Digestive System Abnormality Detection in WCE Images

A Computer-Aided Method for Digestive System Abnormality Detection in WCE Images

Weakly-Supervised Network for Detection of COVID-19 in Chest CT Scans

A Critical Analysis on Perceptual Contrast and Its Use in Visual Information Analysis and Processing

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