Assessment of Crohn’s Disease Lesions in Wireless Capsule Endoscopy Images

Kumar, Rajesh; Zhao, Qian; Seshamani, Sharmishtaa; Mullin, Gerard E.; Hager, Gregory D.; Dassopoulos, T.

doi:10.1109/tbme.2011.2172438

Cited by 84 publications

(77 citation statements)

References 22 publications

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“…In [43], pixel brightness and image texture descriptors together with nonlinear classifier are used to detect celiac disease. In the paper [44], MPEG-7 descriptors for color, texture and edge are developed and tested for Chrons disease. Valdeavilla et.…”

Section: E Classifiers For Detection Of Chrons Disease and Ulcermentioning

confidence: 99%

Image Classifiers in Endoscopy for Detection of Malignancy in Gastro Intestinal Tract

Prashanth¹,

Vani²

2017

IJIGSP

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Abstract-Wireless Capsule Endoscopy (WCE) is one of the methods for examination of gastrointestinal (GI) disorders such as obscure GI bleeding, Crohns disease, polyps etc. WCE has been recognized as a less expensive and painless procedure for the diagnosis of GI tract. This paper examines the various image classifiers designed and developed for the purpose of endoscopy focusing specifically on WCE. It is revealed that designing a suitable image classifier is an important prerequisite for accurate and precise diagnosis of malignancy in WCE. The assessment on various image classifiers used for the diagnosis of pathologies in different parts of GI tract shows that classifiers have reduced the diagnosis time for medical experts and also provided reasonably accurate diagnosis of malignancy. However, correlating classifiers and related pathologies is still observed to be challenging. In view of the fact that early detection may decrease the mortality rate significantly, inclination towards computer aided diagnosis are expected to increase in future. There is a need for advanced research in the development of a robust computer aided diagnosis system, capable of diagnosis of various pathologies in GI tract with higher degree of accuracy and reliability. Further, the study depicts that a direct comparison of results of classifier such as accuracy, prediction, sensitivity, specificity and precision to evaluate its performance is challenging due to diversity of image databases. More research is needed to identify and reduce the uncertainties in the application of image classifier to improve the diagnosis accuracy.

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Section: E Classifiers For Detection Of Chrons Disease and Ulcermentioning

confidence: 99%

Image Classifiers in Endoscopy for Detection of Malignancy in Gastro Intestinal Tract

Prashanth¹,

Vani²

2017

IJIGSP

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“…Depending on the lesions, the decision support systems can be classified to handle bleeding [10], [11], Helicobacter pylori [12], [13], Crohn's disease [7], tumors [14], polyps [15], ulcers [2], and cancers [16], [17]. The commonly used classification models are support vector machine (SVM) [17], [18], neural network [19], fuzzy logic principles [20], clustering-based methods [21], and filter-based methods [22].…”

Section: A Computer-aided Endoscopic Diagnosis Systemsmentioning

confidence: 99%

Computer-Aided Endoscopic Diagnosis Without Human-Specific Labeling

Wang

Cong

Fan

et al. 2016

IEEE Trans. Biomed. Eng.

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Abstract-Goal: Most state-of-the-art computer-aided endoscopic diagnosis methods require pixelwise labeled data to train various supervised machine learning models. However, it is a tedious and time-consuming work to collect sufficient precisely labeled image data. Fortunately, we can easily obtain huge endoscopic medical reports including the diagnostic text and images, which can be considered as weakly labeled data. Methods: In this paper, our motivation is to design a new computer-aided endoscopic diagnosis system without human specific labeling; in comparison with most state of the arts, ours only depends on the endoscopic images with weak labels mined from the diagnostic text. To achieve this, we first cast the endoscopic image folder and included images as bag and instances and represent each instance based on the global bag-of-words model. We then adopt a feature mapping scheme to represent each bag by mining the most suspicious lesion instance from each positive bag automatically. In order to achieve self-online updating from sequential new coming data, an online metric learning method is used to optimize the bag-level classification. Results: Our computer-aided endoscopic diagnosis system achieves an AUC of 0.93 on a new endoscopic image dataset captured from 424 volunteers with more than 12k images. Conclusion: The system performance outperforms other state of the arts when we mine the most positive instances from positive bags and adopt the online phase to mine more information from the unseen bags. Significance: We present the first weakly labeled endoscopic image dataset for computer-aided endoscopic diagnosis and a novel system that is suitable for use in clinical settings.Index Terms-Computer-aided endoscopic diagnosis, multiple instance learning (MIL), online metric learning, weakly labeled data.

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“…Over the past few years, many computer-aided endoscopy diagnosis systems have been proposed, such as feature extraction [Coimbra and Cunha 2006;Wu et al 2007;Riaz et al 2012], feature selection [Cong et al 2015;Huang et al 2008;Li and Meng 2012], lesion classification [Buchner et al 2010;Li and Meng 2009;Kumar et al 2012;Yuan et al 2015;Mamonov et al 2014], video summarization [Chu et al 2010;Mehmood et al 2014;Iakovidis et al 2010], image enhancement [Muto et al 2011;Shahidi et al 2003;Gono et al 2004], and video segmentation [Mackiewicz et al 2008;Shen et al 2012]. Even though most of the existing systems obtain attractive performance, they stand on top of large detailed annotated images.…”

Section: Introductionmentioning

confidence: 99%

Multi-Class Latent Concept Pooling for Computer-Aided Endoscopy Diagnosis

Wang

Cong

Fan

et al. 2017

ACM Trans. Multimedia Comput. Commun. Appl.

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Successful computer-aided diagnosis systems typically rely on training datasets containing sufficient and richly annotated images. However, detailed image annotation is often time consuming and subjective, especially for medical images, which becomes the bottleneck for the collection of large datasets and then building computer-aided diagnosis systems. In this article, we design a novel computer-aided endoscopy diagnosis system to deal with the multi-classification problem of electronic endoscopy medical records (EEMRs) containing sets of frames, while labels of EEMRs can be mined from the corresponding text records using an automatic text-matching strategy without human special labeling. With unambiguous EEMR labels and ambiguous frame labels, we propose a simple but effective pooling scheme called Multi-class Latent Concept Pooling, which learns a codebook from EEMRs with different classes step by step and encodes EEMRs based on a soft weighting strategy. In our method, a computer-aided diagnosis system can be extended to new unseen classes with ease and applied to the standard single-instance classification problem even though detailed annotated images are unavailable. In order to validate our system, we collect 1,889 EEMRs with more than 59K frames and successfully mine labels for 348 of them. The experimental results show that our proposed system significantly outperforms the state-of-the-art methods. Moreover, we apply the learned latent concept codebook to detect the abnormalities in endoscopy images and compare it with a supervised learning classifier, and the evaluation shows that our codebook learning method can effectively extract the true prototypes related to different classes from the ambiguous data.

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Assessment of Crohn’s Disease Lesions in Wireless Capsule Endoscopy Images

Cited by 84 publications

References 22 publications

Image Classifiers in Endoscopy for Detection of Malignancy in Gastro Intestinal Tract

Image Classifiers in Endoscopy for Detection of Malignancy in Gastro Intestinal Tract

Computer-Aided Endoscopic Diagnosis Without Human-Specific Labeling

Multi-Class Latent Concept Pooling for Computer-Aided Endoscopy Diagnosis

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