Wireless capsule endoscopy (WCE) is an effective video technology to diagnose gastrointestinal (GI) disease, such as bleeding. In order to avoid conventional tedious and risky manual review process of long duration WCE videos, automatic bleeding detection schemes are getting importance. In this paper, to investigate bleeding, the analysis of WCE images is carried out in normalized RGB color space as human perception of bleeding is associated with different shades of red. In the proposed method, at first, from the WCE image frame, an efficient region of interest (ROI) is extracted based on interplane intensity variation profile in normalized RGB space. Next, from the extracted ROI, the variation in the normalized green plane is presented with the help of histogram. Features are extracted from the proposed normalized green plane histograms. For classification purpose, the K-nearest neighbors classifier is employed. Moreover, bleeding zones in a bleeding image are extracted utilizing some morphological operations. For performance evaluation, 2300 WCE images obtained from 30 publicly available WCE videos are used in a tenfold cross-validation scheme and the proposed method outperforms the reported four existing methods having an accuracy of 97.86%, a sensitivity of 95.20%, and a specificity of 98.32%.

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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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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