A novel method to detect bleeding frame and region in wireless capsule endoscopy video

Sivakumar, P.; Kumar, Brijesh

doi:10.1007/s10586-017-1584-y

Cited by 19 publications

(15 citation statements)

References 12 publications

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“…Various approaches those are used for the development of automatic bleeding detection methods are VOLUME 8, 2020 This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see http://creativecommons.org/licenses/by/4.0/ based on suspected blood indicator [3], statistical features [8], pixel intensity histogram-based features [5], [9], [22], block-based approaches [6], bag-of-words (BOW) based approach [7], salient-point based approaches, [12], [23] and deep learning architectures [10], [11]. Moreover, computeraided ulcer and erosion detection methods are developed using convolutional neural network (CNN) based architecture [15], completed local binary pattern (LBP), and laplacian pyramid [14], and indexed image based approach [16].…”

Section: Introductionmentioning

confidence: 99%

Multiple Linear Discriminant Models for Extracting Salient Characteristic Patterns in Capsule Endoscopy Images for Multi-Disease Detection

Kundu

Fattah

Wahid

2020

IEEE J. Transl. Eng. Health Med.

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Background: Computer-aided disease detection schemes from wireless capsule endoscopy (WCE) videos have received great attention by the researchers for reducing physicians' burden due to the time-consuming and risky manual review process. While single disease classification schemes are greatly dealt by the researchers in the past, developing a unified scheme which is capable of detecting multiple gastrointestinal (GI) diseases is very challenging due to the highly irregular behavior of diseased images in terms of color patterns. Method: In this paper, a computer-aided method is developed to detect multiple GI diseases from WCE videos utilizing linear discriminant analysis (LDA) based region of interest (ROI) separation scheme followed by a probabilistic model fitting approach. Commonly in training phase, as pixel-labeled images are available in small number, only the image-level annotations are used for detecting diseases in WCE images, whereas pixel-level knowledge, although a major source for learning the disease characteristics, is left unused. In view of learning the characteristic disease patterns from pixel-labeled images, a set of LDA models are trained which are later used to extract the salient ROI from WCE images both in training and testing stages. The intensity patterns of ROI are then modeled by a suitable probability distribution and the fitted parameters of the distribution are utilized as features in a supervised cascaded classification scheme. Results: For the purpose of validation of the proposed multi-disease detection scheme, a set of pixel-labeled images of bleeding, ulcer and tumor are used to extract the LDA models and then, a large WCE dataset is used for training and testing. A high level of accuracy is achieved even with a small number of pixel-labeled images. Conclusion: Therefore, the proposed scheme is expected to help physicians in reviewing a large number of WCE images to diagnose different GI diseases.

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Section: Introductionmentioning

confidence: 99%

Multiple Linear Discriminant Models for Extracting Salient Characteristic Patterns in Capsule Endoscopy Images for Multi-Disease Detection

Kundu

Fattah

Wahid

2020

IEEE J. Transl. Eng. Health Med.

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“…According to [8], Probabilistic neural network is applied to detect the bleeding portion and also improved to recognize bleeding part for achieving more precise results. A superpixel technique has been applied by Sivakumar et al [9] with a Naive Bayes classifier to detect the bleeding region accurately. However, the model has been trained only two statistical features and did not validate with the other exiting techniques.…”

Section: Related Workmentioning

confidence: 99%

Bleeding recognition technique in wireless capsule endoscopy images using fuzzy logic and principal component analysis

Mamun

Ping

Ghosh

et al. 2021

IJECE

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Wireless capsule endoscopy is the most innovative technology to perceive the entire gastrointestinal (GI) tract in recent times. It can diagnose inner diseases like bleeding, ulcer, tumor, Crohn's disease, and polyps. in a discretion way. It creates immense pressure and onus for clinicians to perceive a huge number of image frames, which is time-consuming and makes human oversight errors. Therefore a computer-automated system has been introduced for bleeding detection. A unique fuzzy logic technique is proposed to extract the specified bleeding and non-bleeding information from the image data. A particular quadratic support vector machine (QSVM) classifier is employed to classify the obtained statistical features from the bleeding and non-bleeding images incorporating principal component analysis (PCA). After extensive experiments on clinical data, 98% sensitivity, 98.4% accuracy, 98% specificity, 93% precision, 95.4% F1-score, and 99% negative predicted value have been achieved, which outperforms some of the states of art methods in this regard. It is optimistic that the proposed methodology would significantly contribute to bleeding detection techniques and diminish the additional onus of the physicians.

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“…Initially, a framework was designed to detect bleeding portion in which the specificity and sensitivity were only 41.8% and 21.5%. A superpixel technique has been applied by Sivakumar et al [6] with a Naive Bayes classifier to detect the bleeding region accurately. However, the model has been trained only two statistical features and did not validate with the other exiting techniques.…”

Section: Relative Workmentioning

confidence: 99%

Small intestine bleeding detection using color threshold and morphological operation in WCE images

Mamun

Hossain

Ping

et al. 2021

IJECE

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<span>Wireless capsule endoscopy (WCE) is a significant modern technique for observing the whole gastroenterological tract to diagnose various diseases like bleeding, ulcer, tumor, Crohn's disease, polyps etc in a non-invasive manner. However, it will make a substantial onus for physicians like human oversight errors with time consumption for manual checking of a vast amount of image frames. These problems motivate the researchers to employ a computer-aided system to classify the particular information from the image frames. Therefore, a computer-aided system based on the color threshold and morphological operation has been proposed in this research to recognize specified bleeding images from the WCE. Besides, A unique classifier, quadratic support vector machine (QSVM) has been employed for classifying the bleeding and non-bleeding images with the statistical feature vector in HSV color space. After extensive experiments on clinical data, 95.8% accuracy, 95% sensitivity, 97% specificity, 80% precision, 99% negative predicted value and 85% F1 score has been achieved, which outperforms some of the existing methods in this regard. It is expected that this methodology would bring a significant contribution to the WCE technology. </span>

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A novel method to detect bleeding frame and region in wireless capsule endoscopy video

Cited by 19 publications

References 12 publications

Multiple Linear Discriminant Models for Extracting Salient Characteristic Patterns in Capsule Endoscopy Images for Multi-Disease Detection

Multiple Linear Discriminant Models for Extracting Salient Characteristic Patterns in Capsule Endoscopy Images for Multi-Disease Detection

Bleeding recognition technique in wireless capsule endoscopy images using fuzzy logic and principal component analysis

Small intestine bleeding detection using color threshold and morphological operation in WCE images

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