A Saliency-based Unsupervised Method for Angiectasia Detection in Endoscopic Video Frames

Deeba, Farah; Mohammed, Shahed K.; Bui, Francis M.; Wahid, Khan A.

doi:10.1007/s40846-017-0299-0

Cited by 13 publications

(9 citation statements)

References 27 publications

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“…Finally, we compared the proposed method with Deeba et al's method [ 4 ] in all introduced evaluation metrics on the second and third datasets. It should be noted that the implementation code of this method was shared in GitHub ( https://github.com/Farah-Deeba/Angiectasia-Detection ) by the authors.…”

Section: Resultsmentioning

confidence: 99%

“…Several studies exist for detecting AD lesions in WCE frames. Deeba et al [ 4 ] introduced a saliency map-based method for AD lesion detection. The saliency map is a fusion of color and texture distinctness maps.…”

Section: Related Workmentioning

confidence: 99%

“…Several examples of WCE frames with different lesions are shown in Figure 1 . Since these lesions appear in few frames of a video and usually have a small size compared to the frame size, physicians may miss them during the examination [ 4 ]. In addition, it is a time-consuming and boring task for the physician to check a thousand frames to find pathological lesions [ 5 ].…”

Section: Introductionmentioning

confidence: 99%

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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: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Amiri

Hassanpour

Beghdadi

2021

Journal of Healthcare Engineering

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“…Precious clinical information in some important lesion areas can be displayed on an image [6][7][8][9]. Deeba [6] has used Retinex theory and the salient region detection method for various pathologies, such as stenosis, chylous cysts, lymphangiectasis, polypoid, bleeding, angioectasia, and ulcers.…”

Section: Background and Literaturementioning

confidence: 99%

Feature Selection and Classification of Ulcerated Lesions Using Statistical Analysis for WCE Images

et al. 2017

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Abstract:Wireless capsule endoscopy (WCE) is a technology developed to inspect the whole gastrointestinal tract (especially the small bowel area that is unreachable using the traditional endoscopy procedure) for various abnormalities in a non-invasive manner. However, visualization of a massive number of images is a very time-consuming and tedious task for physicians (prone to human error). Thus, an automatic scheme for lesion detection in WCE videos is a potential solution to alleviate this problem. In this work, a novel statistical approach was chosen for differentiating ulcer and non-ulcer pixels using various color spaces (or more specifically using relevant color bands). The chosen feature vector was used to compute the performance metrics using SVM with grid search method for maximum efficiency. The experimental results and analysis showed that the proposed algorithm was robust in detecting ulcers. The performance in terms of accuracy, sensitivity, and specificity are 97.89%, 96.22%, and 95.09%, respectively, which is promising.

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“…Then, the index of the hemoglobin (IHb), a mode of endoscopic observation that visualizes levels of mucosal circulation [30] is analyzed to monitor the velocity of bloodstream in brachial artery of the interest region. IHb has been studied and reported to obtain useful images for the early detection of gastric cancer, colorectal cancer, and Helicobacter pylori infection by differentiating the circulation condition of mucosa [31,32]. All procedures explained in this paper is done without any human intervention thus the system's output will be verified by human pathologists on its feasibility after the experiment.…”

Section: Introductionmentioning

confidence: 99%

Developing an automatic brachial artery segmentation and bloodstream analysis tool using possibilistic C-means clustering from color doppler ultrasound images

Park

Song

Kim

2021

IJECE

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Automatic segmentation of brachial artery and blood-flow dynamics are important for early detection of cardiovascular disease and other vascular endothelial malfunctions. In this paper, we propose a software that is noise tolerant and fully automatic in segmentation of brachial artery from color Doppler ultrasound images. Possibilistic C-Means clustering algorithm is applied to make the automatic segmentation. We use HSV color model to enhance the contrast of bloodstream area in the input image. Our software also provides index of hemoglobin distribution with respect to the blood flow velocity for pathologists to proceed further analysis. In experiment, the proposed method successfully extracts the target area in 59 out of 60 cases (98.3%) with field expert’s verification.

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A Saliency-based Unsupervised Method for Angiectasia Detection in Endoscopic Video Frames

Cited by 13 publications

References 27 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

Feature Selection and Classification of Ulcerated Lesions Using Statistical Analysis for WCE Images

Developing an automatic brachial artery segmentation and bloodstream analysis tool using possibilistic C-means clustering from color doppler ultrasound images

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