{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,7]],"date-time":"2025-03-07T19:10:09Z","timestamp":1741374609407,"version":"3.38.0"},"reference-count":34,"publisher":"Public Library of Science (PLoS)","issue":"3","license":[{"start":{"date-parts":[[2025,3,7]],"date-time":"2025-03-07T00:00:00Z","timestamp":1741305600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS ONE"],"abstract":"<jats:p>A considerable amount of undesirable factors in the wireless capsule endoscopy (WCE) procedure hinder the proper visualization of the small bowel and take gastroenterologists more time to review. Objective quantitative assessment of different bowel preparation paradigms and saving the physician reviewing time motivated us to present an automatic low-cost statistical model for automatically segmenting of clean and contaminated regions in the WCE images. In the model construction phase, only 20 manually pixel-labeled images have been used from the normal and reduced mucosal view classes of the Kvasir capsule endoscopy dataset. In addition to calculating prior probability, two different probabilistic tri-variate Gaussian distribution models (GDMs) with unique mean vectors and covariance matrices have been fitted to the concatenated RGB color pixel intensity values of clean and contaminated regions separately. Applying the Bayes rule, the membership probability of every pixel of the input test image to each of the two classes is evaluated. The robustness has been evaluated using 5 trials; in each round, from the total number of 2000 randomly selected images, 20 and 1980 images have been used for model construction and evaluation modes, respectively. Our experimental results indicate that accuracy, precision, specificity, sensitivity, area under the receiver operating characteristic curve (AUROC), dice similarity coefficient (DSC), and intersection over union (IOU) are 0.89 \u00b1\u2009 0.07, 0.91 \u00b1\u2009 0.07, 0.73 \u00b1\u2009 0.20, 0.90 \u00b1\u2009 0.12, 0.92\u2009\u00b1\u20090.06, 0.92 \u00b1\u2009 0.05 and 0.86 \u00b1\u2009 0.09, respectively. The presented scheme is easy to deploy for objectively assessing small bowel cleansing score, comparing different bowel preparation paradigms, and decreasing the inspection time. The results from the SEE-AI project dataset and CECleanliness database proved that the proposed scheme has good adaptability.<\/jats:p>","DOI":"10.1371\/journal.pone.0315638","type":"journal-article","created":{"date-parts":[[2025,3,7]],"date-time":"2025-03-07T18:28:01Z","timestamp":1741372081000},"page":"e0315638","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":0,"title":["Multivariate Gaussian Bayes classifier with limited data for segmentation of clean and contaminated regions in the small bowel capsule endoscopy images"],"prefix":"10.1371","volume":"20","author":[{"given":"Vahid","family":"Sadeghi","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7964-0478","authenticated-orcid":true,"given":"Alireza","family":"Mehridehnavi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Maryam","family":"Behdad","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alireza","family":"Vard","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mina","family":"Omrani","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mohsen","family":"Sharifi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yasaman","family":"Sanahmadi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Niloufar","family":"Teyfouri","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"340","published-online":{"date-parts":[[2025,3,7]]},"reference":[{"issue":"6","key":"pone.0315638.ref001","doi-asserted-by":"crossref","first-page":"805","DOI":"10.1016\/j.gie.2013.06.026","article-title":"Wireless capsule endoscopy","volume":"78","author":"ASGE Technology Committee","year":"2013","journal-title":"Gastrointest Endosc"},{"issue":"3","key":"pone.0315638.ref002","first-page":"342","article-title":"A comparative study of two kinds of small bowel cleaning score system for capsule endoscopy","volume":"75","author":"H-B Chen","year":"2012","journal-title":"Acta Gastroenterol Belg"},{"issue":"10","key":"pone.0315638.ref003","doi-asserted-by":"crossref","first-page":"979","DOI":"10.1080\/17474124.2017.1359540","article-title":"Systematic review and meta-analysis: is bowel preparation still necessary in small bowel capsule endoscopy?","volume":"11","author":"DE Yung","year":"2017","journal-title":"Expert Rev Gastroenterol Hepatol"},{"issue":"4","key":"pone.0315638.ref004","doi-asserted-by":"crossref","first-page":"487","DOI":"10.1016\/S0016-5107(04)00003-3","article-title":"Simethicone for small bowel preparation for capsule endoscopy: a systematic, single-blinded, controlled study","volume":"59","author":"J Albert","year":"2004","journal-title":"Gastrointest Endosc"},{"issue":"2","key":"pone.0315638.ref005","doi-asserted-by":"crossref","first-page":"67","DOI":"10.4253\/wjge.v5.i2.67","article-title":"Oral purgative and simethicone before small bowel capsule endoscopy","volume":"5","author":"BJF Rosa","year":"2013","journal-title":"World J Gastrointest Endosc"},{"issue":"9","key":"pone.0315638.ref006","doi-asserted-by":"crossref","first-page":"1711","DOI":"10.3390\/diagnostics11091711","article-title":"Lavage, simethicone, and prokinetics-what to swallow with a video capsule","volume":"11","author":"M Keuchel","year":"2021","journal-title":"Diagnostics (Basel)"},{"issue":"1","key":"pone.0315638.ref007","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1093\/gastro\/goz054","article-title":"Evaluating a combined bowel preparation for small-bowel capsule endoscopy: a prospective randomized-controlled study","volume":"8","author":"SL Hansel","year":"2019","journal-title":"Gastroenterol Rep (Oxf)"},{"issue":"10","key":"pone.0315638.ref008","doi-asserted-by":"crossref","first-page":"102029","DOI":"10.1016\/j.clinre.2022.102029","article-title":"Preparation of small bowel capsule endoscopy (SBCE) with simethicone: a meta-analysis","volume":"46","author":"S Chen","year":"2022","journal-title":"Clin Res Hepatol Gastroenterol"},{"issue":"2","key":"pone.0315638.ref009","doi-asserted-by":"crossref","first-page":"262","DOI":"10.1016\/j.gie.2008.04.016","article-title":"A validation study of 3 grading systems to evaluate small-bowel cleansing for wireless capsule endoscopy: a quantitative index, a qualitative evaluation, and an overall adequacy assessment","volume":"69","author":"C Brotz","year":"2009","journal-title":"Gastrointest Endosc"},{"issue":"6","key":"pone.0315638.ref010","doi-asserted-by":"crossref","first-page":"101612","DOI":"10.1016\/j.clinre.2020.101612","article-title":"Prospective evaluation of third-generation small bowel capsule endoscopy videos by independent readers demonstrates poor reproducibility of cleanliness classifications","volume":"45","author":"X Dray","year":"2021","journal-title":"Clin Res Hepatol Gastroenterol"},{"key":"pone.0315638.ref011","unstructured":"Baatrup G, Nadimi ES. Assessment of bowel cleansing quality in colon capsule endoscopy using machine learning: a pilot study Authors. 2018."},{"issue":"3","key":"pone.0315638.ref012","doi-asserted-by":"crossref","first-page":"613","DOI":"10.3390\/diagnostics12030613","article-title":"Use of U-Net Convolutional Neural Networks for Automated Segmentation of Fecal Material for Objective Evaluation of Bowel Preparation Quality in Colonoscopy","volume":"12","author":"Y-P Wang","year":"2022","journal-title":"Diagnostics (Basel)"},{"issue":"11","key":"pone.0315638.ref013","doi-asserted-by":"crossref","first-page":"e22662","DOI":"10.1016\/j.heliyon.2023.e22662","article-title":"CCRA: A colon cleanliness rating algorithm based on colonoscopy video analysis","volume":"9","author":"Y Bo","year":"2023","journal-title":"Heliyon"},{"key":"pone.0315638.ref014","unstructured":"P. Spyridonos, O. Pujol, J. Vitri, P. Radeva, and H. Vall, \u201cAutomatic Detection of Intestinal Juices in Wireless Capsule Video Endoscopy,\u201d pp. 18\u201321, 2006."},{"key":"pone.0315638.ref015","doi-asserted-by":"crossref","first-page":"152","DOI":"10.1016\/j.optlastec.2018.08.051","article-title":"Reduction of bubble-like frames using a RSS filter in wireless capsule endoscopy video","volume":"110","author":"Q Wang","year":"2019","journal-title":"Opt Laser Technol"},{"issue":"4","key":"pone.0315638.ref016","doi-asserted-by":"crossref","first-page":"E462","DOI":"10.1055\/a-0573-1044","article-title":"Development and validation of an automated algorithm to evaluate the abundance of bubbles in small bowel capsule endoscopy","volume":"6","author":"O Pietri","year":"2018","journal-title":"Endosc Int Open"},{"key":"pone.0315638.ref017","doi-asserted-by":"crossref","first-page":"101364","DOI":"10.1016\/j.imu.2023.101364","article-title":"Segmentation and region quantification of bubbles in small bowel capsule endoscopy images using wavelet transform","volume":"42","author":"V Sadeghi","year":"2023","journal-title":"Informatics Med Unlocked"},{"issue":"5","key":"pone.0315638.ref018","doi-asserted-by":"crossref","first-page":"406","DOI":"10.1055\/s-0030-1256228","article-title":"Description of a novel grading system to assess the quality of bowel preparation in video capsule endoscopy","volume":"43","author":"SJB Van Weyenberg","year":"2011","journal-title":"Endoscopy"},{"issue":"6","key":"pone.0315638.ref019","doi-asserted-by":"crossref","first-page":"E646","DOI":"10.1055\/a-0581-8758","article-title":"Development and validation of a computed assessment of cleansing score for evaluation of quality of small-bowel visualization in capsule endoscopy","volume":"6","author":"E Abou Ali","year":"2018","journal-title":"Endosc Int Open"},{"issue":"8","key":"pone.0315638.ref020","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1007\/s10916-017-0769-5","article-title":"An Automated Self-Learning Quantification System to Identify Visible Areas in Capsule Endoscopy Images","volume":"41","author":"S Hashimoto","year":"2017","journal-title":"J Med Syst"},{"issue":"6","key":"pone.0315638.ref021","doi-asserted-by":"crossref","first-page":"1341","DOI":"10.1109\/TITB.2012.2221472","article-title":"Categorization and segmentation of intestinal content frames for wireless capsule endoscopy","volume":"16","author":"S Segu\u00ed","year":"2012","journal-title":"IEEE Trans Inf Technol Biomed"},{"key":"pone.0315638.ref022","doi-asserted-by":"crossref","unstructured":"Sun Z. Removal of Non-informative Frames for Wireless Capsule Endoscopy Video Segmentation. no. August, pp. 294\u2013299, 2012.","DOI":"10.1109\/ICAL.2012.6308214"},{"issue":"Pt 2","key":"pone.0315638.ref023","first-page":"603","article-title":"Detecting informative frames from wireless capsule endoscopic video using color and texture features","volume":"11","author":"MK Bashar","year":"2008","journal-title":"Med Image Comput Comput Assist Interv"},{"issue":"9","key":"pone.0315638.ref024","doi-asserted-by":"crossref","first-page":"932","DOI":"10.1055\/a-1301-3841","article-title":"A neural network-based algorithm for assessing the cleanliness of small bowel during capsule endoscopy","volume":"53","author":"R Leenhardt","year":"2021","journal-title":"Endoscopy"},{"issue":"1","key":"pone.0315638.ref025","doi-asserted-by":"crossref","first-page":"4417","DOI":"10.1038\/s41598-021-81686-7","article-title":"Development of a deep learning-based software for calculating cleansing score in small bowel capsule endoscopy","volume":"11","author":"JH Nam","year":"2021","journal-title":"Sci Rep"},{"issue":"1","key":"pone.0315638.ref026","doi-asserted-by":"crossref","first-page":"17706","DOI":"10.1038\/s41598-020-74668-8","article-title":"Automatic evaluation of degree of cleanliness in capsule endoscopy based on a novel CNN architecture","volume":"10","author":"R Noorda","year":"2020","journal-title":"Sci Rep"},{"issue":"3","key":"pone.0315638.ref027","first-page":"397","article-title":"Semantic Segmentation Dataset for AI-Based Quantification of Clean Mucosa in Capsule Endoscopy","volume":"58","author":"J-W Ju","year":"2022","journal-title":"Med"},{"issue":"1","key":"pone.0315638.ref028","doi-asserted-by":"crossref","first-page":"142","DOI":"10.1038\/s41597-021-00920-z","article-title":"Kvasir-Capsule, a video capsule endoscopy dataset","volume":"8","author":"PH Smedsrud","year":"2021","journal-title":"Sci Data"},{"issue":"1","key":"pone.0315638.ref029","doi-asserted-by":"crossref","first-page":"e258","DOI":"10.1002\/deo2.258","article-title":"Small bowel capsule endoscopy examination and open access database with artificial intelligence: the SEE-artificial intelligence project","volume":"4","author":"A Yokote","year":"2023","journal-title":"DEN Open"},{"issue":"2","key":"pone.0315638.ref030","doi-asserted-by":"crossref","first-page":"125","DOI":"10.3390\/info11020125","article-title":"Albumentations: Fast and Flexible Image Augmentations","volume":"11","author":"A Buslaev","year":"2020","journal-title":"Information"},{"issue":"10","key":"pone.0315638.ref031","doi-asserted-by":"crossref","first-page":"106002","DOI":"10.1117\/1.JBO.25.10.106002","article-title":"USB capsule endoscope for retrograde imaging of the esophagus","volume":"25","author":"I Martincek","year":"2020","journal-title":"J Biomed Opt"},{"issue":"4","key":"pone.0315638.ref032","doi-asserted-by":"crossref","first-page":"328","DOI":"10.5946\/ce.2018.172","article-title":"Recent Development of Computer Vision Technology to Improve Capsule Endoscopy","volume":"52","author":"J Park","year":"2019","journal-title":"Clin Endosc"},{"issue":"4","key":"pone.0315638.ref033","doi-asserted-by":"crossref","first-page":"884","DOI":"10.1109\/TBCAS.2016.2546838","article-title":"Automated Adaptive Brightness in Wireless Capsule Endoscopy Using Image Segmentation and Sigmoid Function","volume":"10","author":"R Shrestha","year":"2016","journal-title":"IEEE Trans Biomed Circuits Syst"},{"key":"pone.0315638.ref034","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1016\/j.media.2017.07.005","article-title":"A survey on deep learning in medical image analysis","volume":"42","author":"G Litjens","year":"2017","journal-title":"Med Image Anal"}],"container-title":["PLOS ONE"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0315638","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,3,7]],"date-time":"2025-03-07T18:28:21Z","timestamp":1741372101000},"score":1,"resource":{"primary":{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0315638"}},"subtitle":[],"editor":[{"given":"Xiaohui","family":"Zhang","sequence":"first","affiliation":[],"role":[{"role":"editor","vocabulary":"crossref"}]}],"short-title":[],"issued":{"date-parts":[[2025,3,7]]},"references-count":34,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2025,3,7]]}},"URL":"https:\/\/doi.org\/10.1371\/journal.pone.0315638","relation":{},"ISSN":["1932-6203"],"issn-type":[{"type":"electronic","value":"1932-6203"}],"subject":[],"published":{"date-parts":[[2025,3,7]]}}}