Histogram Variance Controlled Bleeding Detectors for Wireless Capsule Endoscopic Images

Ramaraj, M.; Raghavan, S.; Raghunath, V.P.; Khan, Wahid A.

doi:10.1166/jmihi.2014.1297

Cited by 6 publications

(2 citation statements)

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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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“…a 3 = 88.6922. The transformed intensities using formulation (2) are presented in the sixth column and the transformed intensities using formulation (6) are presented in the last column. It is observed from the table that the coefficients using formulation (2) can not separate the bleeding pixel significantly from the non-bleeding ones and it may be treated as non-bleeding if a threshold is drawn.…”

Section: A Prior Lsst Coefficient-vector Extractionmentioning

confidence: 99%

Least Square Saliency Transformation of Capsule Endoscopy Images for PDF Model Based Multiple Gastrointestinal Disease Classification

2020

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Wireless capsule endoscopy (WCE) is an effective video technology to diagnose gastrointestinal (GI) diseases, such as bleeding, ulcer, and tumor. In order to avoid a tedious manual review process of long duration WCE video recordings, automatic disease detection schemes have received significant attention from the researchers. In particular, instead of the conventional approach of dealing with a single disease, developing a unified scheme, which is capable of detecting multiple GI diseases, is getting more importance while being challenging. In this paper, a unified computer-aided scheme is developed for detecting multiple GI diseases from WCE videos based on a proposed least-square saliency transformation (LSST) followed by a probabilistic model-fitting approach. Commonly in the training phase, image-level labeling of images is used, as pixel-level annotations are available only for a small number of images. In view of utilizing the knowledge from pixel-level annotated diseased images, an LSST scheme is proposed to extract a set of optimum prior coefficient-vectors which is later used to capture the salient pixels of interest (POI) in a larger WCE image dataset that do not have pixel-annotations. The intensity distributions of salient POI are modeled by a suitable probability density function (PDF) and the fitted PDF parameters are utilized as features in the proposed supervised hierarchical classification scheme. A large number of WCE images obtained from publicly available WCE videos are used for performance evaluation and it is found that the result obtained by the proposed method outperforms the results obtained in case of some state-of-the-art methods. INDEX TERMS Multiple gastrointestinal disease detection, K-means clustering, least square saliency transformation, quadratic programming, support vector machine. The associate editor coordinating the review of this manuscript and approving it for publication was Derek Abbott. as bleeding is the most common GI disease [3]-[14]. The reported automatic bleeding detection schemes are mainly based on suspected blood indicator [3], histogram-based features [4]-[7], statistical features [8], block-based approaches [7], [9], features from salient points [11], [15] and deep learning framework [12]. In [14], the active and inactive bleeding subgroups are classified based on histogram features and then, separately fed to different fully connected neural networks for bleeding region segmentation. On the other side, automatic ulcer detection schemes are proposed based on convolutional neural network (CNN) based architecture [16], completed local binary patterns (LBP), and laplacian

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Probability density function based modeling of spatial feature variation in capsule endoscopy data for automatic bleeding detection

Kundu

Fattah

2019

Computers in Biology and Medicine

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Histogram Variance Controlled Bleeding Detectors for Wireless Capsule Endoscopic Images

Cited by 6 publications

References 0 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

Least Square Saliency Transformation of Capsule Endoscopy Images for PDF Model Based Multiple Gastrointestinal Disease Classification

Probability density function based modeling of spatial feature variation in capsule endoscopy data for automatic bleeding detection

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