ArticleJournal of healthcare engineering2018
An Automatic Bleeding Frame and Region Detection Scheme for Wireless Capsule Endoscopy Videos Based on Interplane Intensity Variation Profile in Normalized RGB Color Space.
Article in Journal of healthcare engineering, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Convolutional neural network-based segmentation network applied to image recognition of angiodysplasias lesion under capsule endoscopy.World journal of gastroenterology · 2023Article
- RAt-CapsNet: A Deep Learning Network Utilizing Attention and Regional Information for Abnormality Detection in Wireless Capsule Endoscopy.IEEE journal of translational engineering in health and medicine · 2022Article
- Deep Transfer Learning for Automated Intestinal Bleeding Detection in Capsule Endoscopy Imaging.Journal of digital imaging · 2021Article
- Automated bleeding detection in wireless capsule endoscopy images based on color feature extraction from Gaussian mixture model superpixels.Medical & biological engineering & computing · 2021Article
- A Computer-Aided Method for Digestive System Abnormality Detection in WCE Images.Journal of healthcare engineering · 2021Article
- Multiple Linear Discriminant Models for Extracting Salient Characteristic Patterns in Capsule Endoscopy Images for Multi-Disease Detection.IEEE journal of translational engineering in health and medicine · 2020Article
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Authors and funding
3 authors.
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Abstract
Wireless capsule endoscopy (WCE) is an effective video technology to diagnose gastrointestinal (GI) disease, such as bleeding. In order to avoid conventional tedious and risky manual review process of long duration WCE videos, automatic bleeding detection schemes are getting importance. In this paper, to investigate bleeding, the analysis of WCE images is carried out in normalized RGB color space as human perception of bleeding is associated with different shades of red. In the proposed method, at first, from the WCE image frame, an efficient region of interest (ROI) is extracted based on interplane intensity variation profile in normalized RGB space. Next, from the extracted ROI, the variation in the normalized green plane is presented with the help of histogram. Features are extracted from the proposed normalized green plane histograms. For classification purpose, the K-nearest neighbors classifier is employed. Moreover, bleeding zones in a bleeding image are extracted utilizing some morphological operations. For performance evaluation, 2300 WCE images obtained from 30 publicly available WCE videos are used in a tenfold cross-validation scheme and the proposed method outperforms the reported four existing methods having an accuracy of 97.86%, a sensitivity of 95.20%, and a specificity of 98.32%.
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