Bleeding Region Segmentation in Wireless Capsule Endoscopy Images by K-Mean Clustering Technique

Seebutda, Areeya; Sakuncharoenchaiya, Sirilak; Numpacharoen, Kawee; Wiwatwattana, Nuwee; Charoen, Amber; Charoenpong, Theekapun

doi:10.1109/ica-symp56348.2023.10044741

Cited by 3 publications

(3 citation statements)

References 13 publications

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“…Yuan et al, investigated several color histograms, including RGB, HSV, YCbCr, and CIE L*a*b*, and proposed the YCbCr color space to extract bleeding features for discrimination of bleeding images from normal CE images [ 92 ]. The studies in [ 93 , 94 , 95 , 96 ] suggested using the CIE L*a*b* color space for detection or localization of the bleeding region in CE images. Mathew et al proposed a bleeding zone detection system based on the contourlet transform in the CIE XYZ color space [ 97 ].…”

Section: Feature Extractionmentioning

confidence: 99%

“…Using statistical features (such as mean, mode, variance, moment, entropy, energy, skewness, kurtosis, etc. ), several articles [ 1 , 30 , 61 , 71 , 78 , 79 , 94 , 96 , 103 , 109 , 115 , 116 , 122 ] extracted bleeding features from whole CE images. In [ 50 ], statistical color features of bleeding images were extracted from the RGB plane’s first-order histogram.…”

Section: Feature Extractionmentioning

confidence: 99%

“…The studies in [ 54 , 100 ] used a Naïve Bayes classifier to detect bleeding in CE images. In [ 38 , 94 ], another ML algorithm, K-means clustering, was applied to extract important features for summarizing CE video clips. Random Tree and Random Forest are tree-based ML algorithms for making decisions.…”

Section: Algorithmmentioning

confidence: 99%

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Computer-Aided Bleeding Detection Algorithms for Capsule Endoscopy: A Systematic Review

Musha

Hasnat

Mamun

et al. 2023

Sensors

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Capsule endoscopy (CE) is a widely used medical imaging tool for the diagnosis of gastrointestinal tract abnormalities like bleeding. However, CE captures a huge number of image frames, constituting a time-consuming and tedious task for medical experts to manually inspect. To address this issue, researchers have focused on computer-aided bleeding detection systems to automatically identify bleeding in real time. This paper presents a systematic review of the available state-of-the-art computer-aided bleeding detection algorithms for capsule endoscopy. The review was carried out by searching five different repositories (Scopus, PubMed, IEEE Xplore, ACM Digital Library, and ScienceDirect) for all original publications on computer-aided bleeding detection published between 2001 and 2023. The Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) methodology was used to perform the review, and 147 full texts of scientific papers were reviewed. The contributions of this paper are: (I) a taxonomy for computer-aided bleeding detection algorithms for capsule endoscopy is identified; (II) the available state-of-the-art computer-aided bleeding detection algorithms, including various color spaces (RGB, HSV, etc.), feature extraction techniques, and classifiers, are discussed; and (III) the most effective algorithms for practical use are identified. Finally, the paper is concluded by providing future direction for computer-aided bleeding detection research.

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Section: Feature Extractionmentioning

confidence: 99%

Section: Feature Extractionmentioning

confidence: 99%

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Computer-Aided Bleeding Detection Algorithms for Capsule Endoscopy: A Systematic Review

Musha

Hasnat

Mamun

et al. 2023

Sensors

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A Novel Magnetic Spiral Capsule Endoscope Localization Method Based on an Improved Artificial Bee Colony Algorithm

Ye,

Hu,

Zha

et al. 2024

IEEE Sensors J.

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Computer-Aided Bleeding Detection Algorithms for Capsule Endoscopy: A Systematic Review

Musha¹,

Hasnat²,

Mamun³

et al. 2023

Preprint

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Capsule endoscopy (CE) has been a widely used medical imaging tool for the diagnosis of gastrointestinal tract abnormalities like bleeding. But the CE captures a huge number of image frames that are time-consuming and tedious tasks for medical experts to diagnose manually. To address this issue, researchers focused on the computer-aided bleeding detection system to identify bleeding automatically in real-time. This paper presents a systematic review of the available state-of-the-art computer-aided bleeding detection algorithms for capsule endoscopy. The review was carried out by searching five different repositories: Scopus, PubMed, IEEE Xplore, ACM Digital Library, and ScienceDirect for all original publications on computer-aided bleeding detection published between 2001 and 2021. The PRISMA methodology was used to perform the review, and 112 full-texts of scientific papers were reviewed. The contributions of this paper are I) a taxonomy for computer-aided bleeding detection algorithms for capsule endoscopy is identified, II) the available state-of-the-art computer-aided bleeding detection algorithms including various color spaces (RGB, HSV, etc.), feature extraction techniques, and classifiers are discussed, and III) identify the most effective algorithm for practical use cases. Finally, the paper is concluded by providing future direction for computer-aided bleeding detection research.

show abstract

Bleeding Region Segmentation in Wireless Capsule Endoscopy Images by K-Mean Clustering Technique

Cited by 3 publications

References 13 publications

Computer-Aided Bleeding Detection Algorithms for Capsule Endoscopy: A Systematic Review

Computer-Aided Bleeding Detection Algorithms for Capsule Endoscopy: A Systematic Review

A Novel Magnetic Spiral Capsule Endoscope Localization Method Based on an Improved Artificial Bee Colony Algorithm

Computer-Aided Bleeding Detection Algorithms for Capsule Endoscopy: A Systematic Review

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