Stochastic Capsule Endoscopy Image Enhancement

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

doi:10.3390/jimaging4060075

Cited by 12 publications

(4 citation statements)

References 46 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 ). The 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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“…This may cause the imaging performance to drastically decline [6]. The situation deteriorates with capsule endoscopy, primarily due to constraints in power and the capsule's volume [7]. Thus, some image processing techniques must be used to endoscopic images in order to highlight the details and important features for ease of study in clinical settings [8,9].…”

Section: Introductionmentioning

confidence: 99%

Endoscopic Image Enhancement: Wavelet Transform and Guided Filter Decomposition-Based Fusion Approach

Moghtaderi,

Yaghoobian,

Wahid

et al. 2024

J. Imaging

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Endoscopies are helpful for examining internal organs, including the gastrointestinal tract. The endoscope device consists of a flexible tube to which a camera and light source are attached. The diagnostic process heavily depends on the quality of the endoscopic images. That is why the visual quality of endoscopic images has a significant effect on patient care, medical decision-making, and the efficiency of endoscopic treatments. In this study, we propose an endoscopic image enhancement technique based on image fusion. Our method aims to improve the visual quality of endoscopic images by first generating multiple sub images from the single input image which are complementary to one another in terms of local and global contrast. Then, each sub layer is subjected to a novel wavelet transform and guided filter-based decomposition technique. To generate the final improved image, appropriate fusion rules are utilized at the end. A set of upper gastrointestinal tract endoscopic images were put to the test in studies to confirm the efficacy of our strategy. Both qualitative and quantitative analyses show that the proposed framework performs better than some of the state-of-the-art algorithms.

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“…The high similarity between some WCE frames with different abnormalities makes complicate the identification process (see Figure 2). WCE images may also suffer from low lightening, noise, blurriness, and low resolution [14].…”

Section: Introductionmentioning

confidence: 99%

Combining deep features and hand-crafted features for abnormality detection in WCE images

Amiri

Hassanpour

Beghdadi³

2023

Multimed Tools Appl

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In this paper, a computer-aided method is proposed for abnormality detection Wireless Capsule Endoscopy (WCE) video frames. Common abnormalities in WCE images include ulcers, bleeding, Angiodysplasia, Lymphoid Hyperplasia, and polyp. In this paper, deep features and Hand-crafted features are combined to detect these abnormalities in WCE images. There are no sufficient images to train deep structures therefore the ResNet50 pertained model is used to extract deep features. Hand-crafted features are associated with color, shape, and texture. They are extracted from the region of interest (ROI), i.e. suspicious region. The expectation Maximization (EM) algorithm is used to extract more distinct areas in the background as ROI. The expectation Maximization (EM) algorithm is configured in a way that can extract the area with a distinct texture and color as ROI. The EM algorithm is also initialized with a new fast method which leads to an increase in the accuracy of the method. We also used a novel idea to reveal unexpected color changes in the background due to existing lesions as a feature set. A large number of features are created by the method, so the minimum redundancy maximum relevance approach is used to select a subset of more effective features. These selected features are then fed to a Support Vector Machine for classification. The results show that the proposed approach can detect mentioned abnormalities in WCE frames with the accuracy of 97.82%

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Stochastic Capsule Endoscopy Image Enhancement

Cited by 12 publications

References 46 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

Endoscopic Image Enhancement: Wavelet Transform and Guided Filter Decomposition-Based Fusion Approach

Combining deep features and hand-crafted features for abnormality detection in WCE images

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