Automatic lesion detection in wireless capsule endoscopy — A simple solution for a complex problem (original) (raw)
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Automatic lesion detection in wireless capsule endoscopy — A simple solution for a complex problem
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).
Computational and mathematical methods in medicine, 2018
Wireless Capsule Endoscopy (WCE) is a noninvasive diagnostic technique enabling the inspection of the whole gastrointestinal (GI) tract by capturing and wirelessly transmitting thousands of color images. Proprietary software "stitches" the images into videos for examination by accredited readers. However, the videos produced are of large length and consequently the reading task becomes harder and more prone to human errors. Automating the WCE reading process could contribute in both the reduction of the examination time and the improvement of its diagnostic accuracy. In this paper, we present a novel feature extraction methodology for automated WCE image analysis. It aims at discriminating various kinds of abnormalities from the normal contents of WCE images, in a machine learning-based classification framework. The extraction of the proposed features involves an unsupervised color-based saliency detection scheme which, unlike current approaches, combines both point and re...
Wireless Capsule Endoscopy Color Video Segmentation
IEEE Transactions on Medical Imaging, 2000
This paper describes the use of color image analysis to automatically discriminate between oesophagus, stomach, small intestine, and colon tissue in wireless capsule endoscopy (WCE). WCE uses "pill-cam" technology to recover color video imagery from the entire gastrointestinal tract. Accurately reviewing and reporting this data is a vital part of the examination, but it is tedious and time consuming. Automatic image analysis tools play an important role in supporting the clinician and speeding up this process. Our approach first divides the WCE image into subimages and rejects all subimages in which tissue is not clearly visible. We then create a feature vector combining color, texture, and motion information of the entire image and valid subimages. Color features are derived from hue saturation histograms, compressed using a hybrid transform, incorporating the discrete cosine transform and principal component analysis. A second feature combining color and texture information is derived using local binary patterns. The video is segmented into meaningful parts using support vector or multivariate Gaussian classifiers built within the framework of a hidden Markov model. We present experimental results that demonstrate the effectiveness of this method. Index Terms-Color and texture classification, hidden Markov model (HMM), support vector classifier (SVC), video segmentation, wireless capsule endoscopy (WCE). I. INTRODUCTION S TANDARD endoscopy enables a physician to view both ends of a patient's digestive tract including the foodpipe, stomach, duodenum, colon, and terminal ileum. Examination of the remainder of the small intestine was until very recently a difficult procedure. The solution to this problem is the wireless capsule endoscope (WCE), which first appeared in [1] and involves the use of wireless transmission to send images from inside the intestine to the outside world. Since, the device manufactured by Given Imaging Ltd., Israel [2], received FDA clearance in August 2001, over 500 000 examinations have been conducted. The 11 26-mm capsule is swallowed and propelled through the food tract by normal peristalsis. One end of the capsule contains an optical dome with white light emitting diodes and a color camera that captures two images (256 256 pixels) a second. These images are compressed using JPEG and relayed Manuscript
Abnormality Detection from Wireless Capsule Endoscopy Images
2018
In Medical fields, the doctors use different types of imaging technologies among those Wireless Capsule Endoscopy is the one, it used to capture images from the patient body during examination time. Wireless Capsule Endoscopy is prevailing technology which is used by Gasterologist doctors to examine the human digestive system. During the investigation time, more than 57,000 images can be generated and then the doctor examines the images frame by frame to detect mucosal abnormalities (i.e Ulcer, Erosion, erthema, polyp, bleeding...etc). In fact, this is a boring and it takes a lot of time even for a skillful gastrologist doctor. In this paper, different existing abnormal image detection techniques are studied in detail. Recently, the Wireless Capsule Endoscopy (WCE) is an active research area in medical domain. Various research works have been done aiming to develop self-acting algorithms for abnormality detection using color, texture analyses, and other techniques. This paper more f...
Computer‐aided diagnosis system for ulcer detection in wireless capsule endoscopy images
IET Image Processing, 2019
Wireless capsule endoscopy (WCE) has revolutionized the diagnosis and treatment of gastrointestinal tract, especially the small intestine which is unreachable by traditional endoscopies. The drawback of the WCE is that it produces a large amount of images to be inspected by the clinicians. Hence, the design of a computer-aided diagnosis (CAD) system will have a great potential to help reducing the diagnosis time and improve the detection accuracy. To address this problem, we propose a CAD system for automatic detection of ulcer in WCE images. Firstly, we enhance the input images to be better exploited in the main steps of the proposed method. Afterwards, segmentation using saliency map based texture and colour is applied to the WCE images in order to highlight ulcerous regions. Then, inspired by the existing feature extraction approaches, a new one has been proposed for the recognition of the segmented regions. Finally, a new recognition scheme is proposed based on hidden markov model using the classification scores of the conventional methods (support vector machine, multilayer perceptron and random forest) as observations. Experimental results with two different datasets show that the proposed method gives promising results.
CoLD: a versatile detection system for colorectal lesions in endoscopy video-frames
Computer Methods and Programs in Biomedicine, 2003
In this paper, we present CoLD (colorectal lesions detector) an innovative detection system to support colorectal cancer diagnosis and detection of pre-cancerous polyps, by processing endoscopy images or video frame sequences acquired during colonoscopy. It utilizes second-order statistical features that are calculated on the wavelet transformation of each image to discriminate amongst regions of normal or abnormal tissue. An artificial neural network performs the classification of the features. CoLD integrates the feature extraction and classification algorithms under a graphical user interface, which allows both novice and expert users to utilize effectively all system's functions. It has been developed in close cooperation with gastroenterology specialists and has been tested on various colonoscopy videos. The detection accuracy of the proposed system has been estimated to be more than 95%. As it has been resulted, it can be used as a supplementary diagnostic tool for colorectal lesions.
Color-based template selection for detection of gastric abnormalities in video endoscopy
Biomedical Signal Processing and Control, 2020
Computer-aided diagnosis of gastric diseases from endoscopy frames is an important task. It facilitates both the patient and gastroenterologist in terms of time, money and most important health. Colors are the basic visual features of endoscopic images and also provide clues about abnormal regions in endoscopy frames. A variety of color spaces available for representation of color frames. However, we are not certain about which color space is more suitable for representing color features of gastric images. This paper presents a comparison of color features in different color spaces for detection of abnormal areas in chromoendoscopy (CH) frames. In addition, the CH images are segmented by using an existing color-difference based segmentation method Delta E (E). A framework for automatic segmentation is presented for endoscopy images by selecting a template image in E by using trained models. For classification, colors features are also merged with texture descriptors. The support vector machine (SVM) classifier is trained on color features and also the hybrid color combined texture characteristics. Then the trained classifier is used to group CH frames into abnormal and normal classes. E with manual template selection has achieved 57.44% accuracy and 56.88% accuracy with the automated process. Moreover, the suggested method achieves 86.6% accuracy and 0.91 area under the curve for the classification of gastric lesions.
Bleeding detection in wireless capsule endoscopy videos - Color versus texture features
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.