Automated Analysis of Endoscopic Images (original) (raw)
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Multiple bleeding detection in wireless capsule endoscopy
Signal, Image and Video Processing, 2018
Wireless capsule endoscopy (WCE) is an emerging technology that aims to detect pathology in the patient gastrointestinal tract. Physicians can use WCE to detect various gastrointestinal diseases at early stages. However, the diagnosis is tedious because it requires reviewing hundreds of frames extracted from the captured video. This tedious task has promoted researchers' efforts to propose automated diagnosis tools of WCE frames in order to detect symptoms of gastrointestinal diseases. In this paper, we propose an automatic multiple bleeding spots detection using WCE video. The proposed approach relies on two main components: (1) a feature extraction intended to capture the visual properties of the multiple bleeding spots, and (2) a supervised and unsupervised learning techniques which aim to accurately recognize multiple bleeding.
Segmentation of bleeding regions in wireless capsule endoscopy for detection of informative frames
Biomedical Signal Processing and Control, 2019
Wireless capsule endoscopy (WCE) is an effective mean for diagnosis of gastrointestinal disorders. Detection of informative scenes in WCE video could reduce the length of transmitted videos and help the diagnosis procedure. In this paper, we investigate the problem of simplification of neural networks for automatic bleeding region detection inside capsule endoscopy device. Suitable color channels are selected as neural networks inputs, and image classification is conducted using a multi-layer perceptron (MLP) and a convolutional neural network (CNN) separately. Both CNN and MLP structures are simplified to reduce the number of computational operations. Performances of two simplified networks are evaluated on a WCE bleeding image dataset using the DICE score. Simulation results show that applying simplification methods on both MLP and CNN structures reduces the number of computational operations significantly with AUC greater than 0.97. Although CNN performs better in comparison with simplified MLP, the simplified MLP segments bleeding regions with a significantly smaller number of computational operations. Concerning the importance of having a simple structure or a more accurate model, each of the designed structures could be selected for inside capsule implementation.
A technique for blood detection in wireless capsule endoscopy images
2009 17th European Signal Processing Conference, 2009
Wireless capsule endoscopy is an innovative technology for visualizing anomalies in the gastrointestinal tract, useful to replace traditional endoscopic diagnosis. Its advantages are related to the capability to reach the duodenum and small intestine, while eliminating the discomfort of patients. The time spent by a physician analyzing the results of wireless capsule endoscopy video can vary between 45 and 180 minutes, limiting its widespread diffusion. Therefore, methods able to perform an automatic pre-screening of images of interest are necessary. This paper presents an innovative technique to detect bleeding regions in wireless capsule endoscopy video. Experimental results show that the proposed algorithm exhibits a low false alarm rate, and is effective at reducing the time needed to analyze video sequences.
Image And Pixel Based Scheme For Bleeding Detection In Wireless Capsule Endoscopy Images
Advances in Intelligent Systems and Computing, 2016
Bleeding detection techniques that are widely used in digital image analysis can be categorized in 3 main types: image based, pixel based and patch based. For computer-aided diagnosis of bleeding detection in Wireless Capsule Endoscopy (WCE), the most efficient choice among these remains still a problem. In this work, different types of Gastro intestinal bleeding problems:Angiodysplasia, Vascular ecstasia and Vascular lesions detected through WCE are discussed. Effective image processing techniques for bleeding detection in WCE employing both image based and pixel based techniques have been presented. The quantitative analysis of the parameters such as accuracy,sensitivity and specificity shows that YIQ and HSV are suitable color models; while LAB color model incurs low value of sensitivity. Statistical based measurements achieves higher accuracy and specificity with better computation speed up as compared to other models. Classification using K-Nearest Neighbor is deployed to verify the performance. The results obtained are compared and evaluated through the confusion matrix.
Detection of Intestinal Bleeding in Wireless Capsule Endoscopy using Machine Learning Techniques
2020
In presenting the thesis in partial fulfillment of the requirements for a postgraduate degree from the University of Saskatchewan, I agree that the libraries of this university may make it freely available for inspection. I further agree that permission for copying of this thesis in any manner, in whole or in part, for scholarly purposes may be granted by the professor or professors who supervised my thesis work or, in their absence, by the head of the department or the dean of the college in which my thesis work was done. It is understood that any copying or publication or use of this thesis or parts thereof for financial gain shall not be allowed without my written permission. It is also understood that due recognition shall be given to me and to the University of Saskatchewan in any scholarly use which may be made of any material in my thesis/dissertation.
Improving CAD Hemorrhage Detection in Capsule Endoscopy
Journal of Biomedical Science and Engineering, 2021
This study explores an automated framework to assist the recognition of hemorrhage traces and bleeding lesions in video streams of small bowel capsule endoscopy (SBCE). The proposed methodology aims to achieve fast image control (<10 minutes), save valuable time of the physicians, and enable high performance diagnosis. A specialized elimination algorithm excludes all identical consecutive frames by utilizing the difference of gray levels in pixel luminance. An image filtering algorithm is proposed based on an experimentally calculated bleeding index and blood-color chart, which inspects all remaining frames of the footage and identifies pixels that reflect active or potential hemorrhage in color. The bleeding index and blood-color chart are estimated of the chromatic thresholds in RGB and HSV color spaces, and have been extracted after experimenting with more than 3200 training images, derived from 99 videos of a pool of 138 patients. The dataset has been provided by a team of ex...
A statistical feature based novel method to detect bleeding in wireless capsule endoscopy images
2014 International Conference on Informatics, Electronics & Vision (ICIEV), 2014
Wireless capsule endoscopy (WCE) is a recently developed technology to detect small intestine diseases, such as bleeding. In this paper, a scheme for automatic bleeding detection from WCE video is proposed based on different statistical measures computed from a new red to green (R/G) pixel ratio intensity plane of RGB color images. Different statistical parameters, namely mean, mode, maximum, minimum, skewness, median, variance, and kurtosis are used to extract variation in spatial characteristics in R/G intensity plane of bleeding and non-bleeding WCE RGB images. Depending on the ability to provide significantly distinguishable characteristics, in the proposed feature vector, median, variance, and kurtosis of R/G ratio values corresponding to a WCE image are considered. For the purpose of classification, K-nearest neighbor (KNN) classifier is employed. From extensive experimentation on several WCE videos collected from a publicly available database, it is observed that the proposed method can successfully detect bleeding and non-bleeding images with high level of accuracy, sensitivity and specificity in comparison to that of some of the existing methods.
Computer-Aided Bleeding Detection Algorithms for Capsule Endoscopy: A Systematic Review
Capsule endoscopy (CE) has been a widely used medical imaging tool for the diagnosis of gastrointestinal tract abnormalities like bleeding. But the CE captures a huge number of image frames that are time-consuming and tedious tasks for medical experts to diagnose manually. To address this issue, researchers focused on the computer-aided bleeding detection system to identify bleeding automatically in real-time. This paper presents a systematic review of the available state-of-the-art computer-aided bleeding detection algorithms for capsule endoscopy. The review was carried out by searching five different repositories: Scopus, PubMed, IEEE Xplore, ACM Digital Library, and ScienceDirect for all original publications on computer-aided bleeding detection published between 2001 and 2021. The PRISMA methodology was used to perform the review, and 112 full-texts of scientific papers were reviewed. The contributions of this paper are I) a taxonomy for computer-aided bleeding detection algori...
Automatic bleeding detection in wireless capsule endoscopy based on RGB pixel intensity ratio
2014 International Conference on Electrical Engineering and Information & Communication Technology, 2014
Wireless capsule endoscopy (WCE) is one of the most effective technologies to diagnose gastrointestinal (GI) diseases, such as bleeding in GI tract. Because of long duration of WCE video containing large number images, it is a burden for clinician to detect diseases in real time. In this paper, an automatic bleeding image detection method is proposed utilizing the variation of pixel intensities in RGB color planes. Based on statistical behavior of bleeding and non-bleeding pixel intensities in terms of pixel intensity ratio in different planes, distinguishing color texture feature of an image is developed. Support vector machine (SVM) classifier is employed to detect bleeding and nonbleeding images from WCE videos. From extensive experimenttation on real time WCE video recordings, it is found that the proposed method can accurately detect bleeding images with high sensitivity and specificity.
Wireless Capsule Endoscopy is a technology used to examine and view the gastro intestinal tract. A methodology for the detection of bleeding and non-bleeding regions is proposed here. The edge regions are first detected and then removed before identifying the bleeding regions. The edge and the bleeding regions have the same hue value and also the bleeding and non-bleeding regions have same luminance. The canny edge detection algorithm is used to detect edges since it have the ability to detect more edge pixels and preserves more bleeding regions. After the edge detection the regions are segmented by using super-pixel segmentation. Here Statistical features and Texture features are extracted from Gray Level Co-occurrence Matrix. Finally the bleeding and non-bleeding regions are classified by using the Artificial Neural Networks.