Automated Analysis of Endoscopic Images

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...

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.

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...

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.

Wiley, 2019

Wireless capsule endoscopy (WCE) is an effective technology that can be used to make a gastrointestinal (GI) tract diagnosis of various lesions and abnormalities. Due to a long time required to pass through the GI tract, the resulting WCE data stream contains a large number of frames which leads to a tedious job for clinical experts to perform a visual check of each and every frame of a complete patient's video footage. In this paper, an automated technique for bleeding detection based on color and texture features is proposed. The approach combines the color information which is an essential feature for initial detection of frame with bleeding. Additionally, it uses the texture which plays an important role to extract more information from the lesion captured in the frames and allows the system to distinguish finely between borderline cases. The detection algorithm utilizes machine-learning-based classification methods, and it can efficiently distinguish between bleeding and nonbleeding frames and perform pixel-level segmentation of bleeding areas in WCE frames. The performed experimental studies demonstrate the performance of the proposed bleeding detection method in terms of detection accuracy, where we are at least as good as the state-of-the-art approaches. In this research, we have conducted a broad comparison of a number of different state-of-the-art features and classification methods that allows building an efficient and flexible WCE video processing system.

Journal of Biomedical Optics, 2016

We propose two automatic methods for detecting bleeding in wireless capsule endoscopy videos of the small intestine. The first one uses solely the color information, whereas the second one incorporates the assumptions about the blood spot shape and size. The original idea is namely the definition of a new color space that provides good separability of blood pixels and intestinal wall. Both methods can be applied either individually or their results can be fused together for the final decision. We evaluate their individual performance and various fusion rules on real data, manually annotated by an endoscopist.

IEEE journal of translational engineering in health and medicine, 2018

Wireless capsule endoscopy (WCE) is the most advanced technology to visualize whole gastrointestinal (GI) tract in a non-invasive way. But the major disadvantage here, it takes long reviewing time, which is very laborious as continuous manual intervention is necessary. In order to reduce the burden of the clinician, in this paper, an automatic bleeding detection method for WCE video is proposed based on the color histogram of block statistics, namely CHOBS. A single pixel in WCE image may be distorted due to the capsule motion in the GI tract. Instead of considering individual pixel values, a block surrounding to that individual pixel is chosen for extracting local statistical features. By combining local block features of three different color planes of RGB color space, an index value is defined. A color histogram, which is extracted from those index values, provides distinguishable color texture feature. A feature reduction technique utilizing color histogram pattern and principal...

2014 IEEE International Conference on Image Processing (ICIP), 2014

Wireless capsule endoscopy (WCE) is performed with a swallowable miniature optical endoscope which transmits color images wirelessly during its journey in the gastrointestinal tract. In this paper we present a computationally efficient and effective approach to cope with automatic detection of possible abnormalities in the WCE videos and consequently with the reduction of the time required for the WCE inspection. It involves automatic detection of salient points based on color information and supervised classification of simple color vectors extracted from the neighborhood of each point. The experiments performed aim to determine the optimal color space components for feature extraction, and identification of abnormalities. Main advantages of this approach are its computational efficiency, its sensitivity to detect small lesions, and its generality. The results obtained from experimentation with a dataset with various types of abnormalities and non-ideal normal frames, approximate 0.9 in terms of the area under receiver operating characteristic (ROC).

Journal of Medical and Biological Engineering, 2017

Wireless capsule endoscopy (WCE) offers noninvasive means to diagnose intestinal anomalies like bleeding. However, one of the problems here is the time consuming complex reviewing process which calls for automatic computer aided bleeding detection techniques to reduce the burden of the physicians. In this paper, pixel based holistic feature extraction scheme is proposed for bleeding frame detection of WCE recordings. Unlike conventional methods, instead of directly using RGB (red, green, blue) color space, a transform color domain is introduced. Higher and lower order statistical analysis on that composite color domain are carried out to extract features from the given WCE image. Feature-based supervised classification using support vector machine is performed to differentiate bleeding and non-bleeding images. Next, in order to improve the bleeding frame detection performance in WCE video, a post-processing scheme is developed utilizing the variation in temporal characteristics of consecutive frames. Finally, a zone detection algorithm is proposed to identify bleeding regions in the detected bleeding images where some morphological operations are also used. Extensive experimentation is carried out on a numerous number of WCE images and videos. It is observed that the proposed algorithm can detect bleeding frame and zones from WCE video recordings with a satisfactory level of performance.