Empirical Comparison of Visual Descriptors for Multiple Bleeding Spots Recognition in Wireless Capsule Endoscopy Video

Alotaibi, Sarah; Qasim, Sahar; Bchir, Ouiem; Ismail, Mohamed Maher Ben

doi:10.1007/978-3-642-40246-3_50

Cited by 7 publications

(7 citation statements)

References 14 publications

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“…34 A comparative study between colour and texture features indicate that the colour features investigated can be more discriminative for detection of multiple bleeding spots. 35 A summary of state-of-the-art methods for detecting haemorrhage is provided in Table 1.…”

Section: Haemorrhage Detectionmentioning

confidence: 99%

“…72 Other studies have addressed the problem of reducing the review time indirectly, using automatic detection of the transition points between the different parts of Results from the majority of the studies were quantified in terms of accuracy, and/or sensitivity and specificity. The AUROC was adopted in only two studies 17,35 instead of the conventional accuracy, which was not available. *Deals also with ulcer detection.…”

Section: Data Miningmentioning

confidence: 99%

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Software for enhanced video capsule endoscopy: challenges for essential progress

Iakovidis

Koulaouzidis

2015

Nat Rev Gastroenterol Hepatol

189

113

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Video capsule endoscopy (VCE) has revolutionized the diagnostic work-up in the field of small bowel diseases. Furthermore, VCE has the potential to become the leading screening technique for the entire gastrointestinal tract. Computational methods that can be implemented in software can enhance the diagnostic yield of VCE both in terms of efficiency and diagnostic accuracy. Since the appearance of the first capsule endoscope in clinical practice in 2001, information technology (IT) research groups have proposed a variety of such methods, including algorithms for detecting haemorrhage and lesions, reducing the reviewing time, localizing the capsule or lesion, assessing intestinal motility, enhancing the video quality and managing the data. Even though research is prolific (as measured by publication activity), the progress made during the past 5 years can only be considered as marginal with respect to clinically significant outcomes. One thing is clear-parallel pathways of medical and IT scientists exist, each publishing in their own area, but where do these research pathways meet? Could the proposed IT plans have any clinical effect and do clinicians really understand the limitations of VCE software? In this Review, we present an in-depth critical analysis that aims to inspire and align the agendas of the two scientific groups.

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Section: Haemorrhage Detectionmentioning

confidence: 99%

Section: Data Miningmentioning

confidence: 99%

Software for enhanced video capsule endoscopy: challenges for essential progress

Iakovidis

Koulaouzidis

2015

Nat Rev Gastroenterol Hepatol

189

113

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“…Several visual descriptions have been suggested in the literature. Some of these characters are common, while some apply to particularrequests [10]. However, there is a difference among the user's semantic interest and the visual cue extracted by the reporter.…”

Section: Introductionmentioning

confidence: 99%

Modified cuckoo search algorithm: Feature subset selection &Shape,Color and Texture Features Descriptors for Content-Based Image Retrieval

R.V.Sudha¹,

Meena²,

Jayavadivel³

et al. 2021

JUSST

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With the recent advances in knowledge, the complication of multimedia has increased expressively and new areas of research have opened up in search of new multimedia content. Content-based image retrieval (CBIR) are used to extract images associated with image queries (IQs) from huge databases. The CBIR schemes accessible at present have limited functionality because they only have a partial number of functions. This document presents an improved cookie detection algorithm with coarse sentences for processing large amounts of data using selected examples. The improved cuckoo detection algorithm mimics the behavior of brood attachment parasites in some cuckoo species, including some birds. Modified cuckoo recognition uses approximate set theory to create a fitness function that takes into account the sum of features and the quality of classification as a small amount. For an image entered as IQ from a database, distance metrics are used to find the appropriate image. This is the central idea of CBIR. The projected CBIR method is labelled and can extract shape features based on the RGB color using the and canny Edge (CED) and neutrosophic clustering algorithm scheme. After YCbCrcolor cut, and the CED to get the features to extract the vascular matrix. The combination of these techniques improves the efficiency of the CBR image recovery infrastructure. In this thesis recursive neural network techniques are used to measure the similarity. In addition, the accuracy of the results is: The recall score is measured to evaluate system performance. The proposed CBIR system provides more precise and accurate values than the complex CBIR system.

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“…First, algorithms based on the extraction of well-identified features based on SVM. [6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21] Second, algorithms based on deep neural networks. [22][23][24][25][26][27] The latter obtain better classification performance but need more data in order to be trained properly.…”

Section: Introductionmentioning

confidence: 99%

CrohnIPI: An endoscopic image database for the evaluation of automatic Crohn's disease lesions recognition algorithms

Vallée

Maissin²,

Coutrot

et al. 2020

Medical Imaging 2020: Biomedical Applications in Molecular, Structural, and Functional Imaging

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Wireless capsule endoscopy (WCE) allows medical doctors to examine the interior of the small intestine with a noninvasive procedure. This methodology is particularly important for Crohn's disease (CD), where an early diagnosis improves treatment outcomes. The counting and identification of CD lesions in WCE videos is a time-consuming process for medical experts. In the era of deep-learning many automatic WCE lesion classifiers, requiring annotated data, have been developed. However, benchmarking classifiers is difficult due to the lack of standard evaluation data. Most detection algorithms are evaluated on private datasets or on unspecified subsets of public databases. To help the development and comparison of automatic CD lesion classifiers, we release CrohnIPI, a dataset of 3498 images, independently reviewed by several experts. It contains 60.55% of non-pathological images and 38.85% of pathological images with 7 different types of CD lesions. A part of these images are multilabeled. The dataset is balanced between pathological images and non-pathological ones and split into two subsets for training and testing models. This database will progressively be enriched over the next few years in aim to make the automatic detection algorithms converge to the most accurate system possible and to consolidate their evaluation.

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Empirical Comparison of Visual Descriptors for Multiple Bleeding Spots Recognition in Wireless Capsule Endoscopy Video

Cited by 7 publications

References 14 publications

Software for enhanced video capsule endoscopy: challenges for essential progress

Software for enhanced video capsule endoscopy: challenges for essential progress

Modified cuckoo search algorithm: Feature subset selection &Shape,Color and Texture Features Descriptors for Content-Based Image Retrieval

CrohnIPI: An endoscopic image database for the evaluation of automatic Crohn's disease lesions recognition algorithms

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