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"createdByUser": { "id": 1223851, "username": "lavneet", "name": "Lavneet", "links": { "alternate": { "href": "https://www.zotero.org/lavneet", "type": "text/html" } } }, "creatorSummary": "Shen et al.", "parsedDate": "2012-01", "numChildren": 1 }, "data": { "key": "DTBEJADT", "version": 1, "itemType": "journalArticle", "title": "Wireless capsule endoscopy video segmentation using an unsupervised learning approach based on probabilistic latent semantic analysis with scale invariant features", "creators": [ { "creatorType": "author", "firstName": "Yao", "lastName": "Shen" }, { "creatorType": "author", "firstName": "Parthasarathy Partha", "lastName": "Guturu" }, { "creatorType": "author", "firstName": "Bill P", "lastName": "Buckles" } ], "abstractNote": "Since wireless capsule endoscopy (WCE) is a novel technology for recording the videos of the digestive tract of a patient, the problem of segmenting the WCE video of the digestive tract into subvideos corresponding to the entrance, stomach, small intestine, and large intestine regions is not well addressed in the literature. A selected few papers addressing this problem follow supervised leaning approaches that presume availability of a large database of correctly labeled training samples. Considering the difficulties in procuring sizable WCE training data sets needed for achieving high classification accuracy, we introduce in this paper an unsupervised learning approach that employs Scale Invariant Feature Transform (SIFT) for extraction of local image features and the probabilistic latent semantic analysis (pLSA) model used in the linguistic content analysis for data clustering. Results of experimentation indicate that this method compares well in classification accuracy with the state-of-the-art supervised classification approaches to WCE video segmentation.", "publicationTitle": "IEEE transactions on information technology in biomedicine: a publication of the IEEE Engineering in Medicine and Biology Society", "publisher": "", "place": "", "date": "Jan 2012", "volume": "16", "issue": "1", "section": "", "partNumber": "", "partTitle": "", "pages": "98-105", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "IEEE Trans Inf Technol Biomed", "DOI": "10.1109/TITB.2011.2171977", "citationKey": "", "url": "", "accessDate": "", "PMID": "", "PMCID": "", "ISSN": "1558-0032", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "", "libraryCatalog": "NCBI PubMed", "callNumber": "", "rights": "", "extra": "PMID: 22010158", "tags": [ { "tag": "Algorithms", "type": 1 }, { "tag": "Artificial Intelligence", "type": 1 }, { "tag": "Capsule Endoscopy", "type": 1 }, { "tag": "Cluster Analysis", "type": 1 }, { "tag": "Humans", "type": 1 }, { "tag": "Image Processing, Computer-Assisted", "type": 1 }, { "tag": "Semantics", "type": 1 }, { "tag": "Videotape Recording", "type": 1 } ], "collections": [ "9DPJMNEC" ], "relations": {}, "dateAdded": "2012-12-10T07:29:28Z", "dateModified": "2012-12-10T07:29:28Z" } }, { "key": "RRTF4TDV", "version": 2, "library": { "type": "group", "id": 126110, "name": "Lavneet ARENA", "links": { "alternate": { "href": "https://www.zotero.org/groups/lavneet_arena", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/126110/items/RRTF4TDV", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/lavneet_arena/items/RRTF4TDV", "type": "text/html" }, "up": { "href": "https://api.zotero.org/groups/126110/items/38PKMK53", "type": "application/json" } }, "meta": { "createdByUser": { "id": 1223851, "username": "lavneet", "name": "Lavneet", "links": { "alternate": { "href": "https://www.zotero.org/lavneet", "type": "text/html" } } } }, "data": { "key": "RRTF4TDV", "version": 2, "parentItem": "38PKMK53", "itemType": "attachment", "linkMode": "imported_url", "title": "IEEE Xplore Abstract Record", "accessDate": "2012-12-10T07:29:22Z", "url": "http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=4530644&url=http%3A%2F%2Fieeexplore.ieee.org%2Fxpls%2Fabs_all.jsp%3Farnumber%3D4530644", "note": "", "contentType": "text/html", "charset": "utf-8", "filename": "login.html", "md5": "ebac04343a04b59c139d4f460cfdf72b", "mtime": 1355124563999, "tags": [], "relations": {}, "dateAdded": "2012-12-10T07:29:22Z", "dateModified": "2012-12-10T07:29:22Z" } }, { "key": "38PKMK53", "version": 1, "library": { "type": "group", "id": 126110, "name": "Lavneet ARENA", "links": { "alternate": { "href": "https://www.zotero.org/groups/lavneet_arena", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/126110/items/38PKMK53", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/lavneet_arena/items/38PKMK53", "type": "text/html" } }, "meta": { "createdByUser": { "id": 1223851, "username": "lavneet", "name": "Lavneet", "links": { "alternate": { "href": "https://www.zotero.org/lavneet", "type": "text/html" } } }, "creatorSummary": "Mackiewicz et al.", "parsedDate": "2008-12", "numChildren": 1 }, "data": { "key": "38PKMK53", "version": 1, "itemType": "journalArticle", "title": "Wireless Capsule Endoscopy Color Video Segmentation", "creators": [ { "creatorType": "author", "firstName": "M.", "lastName": "Mackiewicz" }, { "creatorType": "author", "firstName": "J.", "lastName": "Berens" }, { "creatorType": "author", "firstName": "M.", "lastName": "Fisher" } ], "abstractNote": "This paper describes the use of color image analysis to automatically discriminate between oesophagus, stomach, small intestine, and colon tissue in wireless capsule endoscopy (WCE). WCE uses ldquopill-camrdquo technology to recover color video imagery from the entire gastrointestinal tract. Accurately reviewing and reporting this data is a vital part of the examination, but it is tedious and time consuming. Automatic image analysis tools play an important role in supporting the clinician and speeding up this process. Our approach first divides the WCE image into subimages and rejects all subimages in which tissue is not clearly visible. We then create a feature vector combining color, texture, and motion information of the entire image and valid subimages. Color features are derived from hue saturation histograms, compressed using a hybrid transform, incorporating the discrete cosine transform and principal component analysis. A second feature combining color and texture information is derived using local binary patterns. The video is segmented into meaningful parts using support vector or multivariate Gaussian classifiers built within the framework of a hidden Markov model. We present experimental results that demonstrate the effectiveness of this method.", "publicationTitle": "IEEE Transactions on Medical Imaging", "publisher": "", "place": "", "date": "dec. 2008", "volume": "27", "issue": "12", "section": "", "partNumber": "", "partTitle": "", "pages": "1769 -1781", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "", "DOI": "10.1109/TMI.2008.926061", "citationKey": "", "url": "", "accessDate": "", "PMID": "", "PMCID": "", "ISSN": "0278-0062", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "", "libraryCatalog": "IEEE Xplore", "callNumber": "", "rights": "", "extra": "", "tags": [ { "tag": "Algorithms", "type": 1 }, { "tag": "Capsule Endoscopes", "type": 1 }, { "tag": "Capsule Endoscopy", "type": 1 }, { "tag": "Color", "type": 1 }, { "tag": "Colorimetry", "type": 1 }, { "tag": "Data Compression", "type": 1 }, { "tag": "Humans", "type": 1 }, { "tag": "Image Enhancement", "type": 1 }, { "tag": "Image Interpretation, Computer-Assisted", "type": 1 }, { "tag": "Lower Gastrointestinal Tract", "type": 1 }, { "tag": "Markov Chains", "type": 1 }, { "tag": "Normal Distribution", "type": 1 }, { "tag": "Pattern Recognition, Automated", "type": 1 }, { "tag": "Upper Gastrointestinal Tract", "type": 1 }, { "tag": "automatic image analysis tools", "type": 1 }, { "tag": "biological organs", "type": 1 }, { "tag": "biomedical optical imaging", "type": 1 }, { "tag": "color image analysis", "type": 1 }, { "tag": "color video imagery", "type": 1 }, { "tag": "color video segmentation", "type": 1 }, { "tag": "discrete cosine transform", "type": 1 }, { "tag": "discrete cosine transforms", "type": 1 }, { "tag": "endoscopes", "type": 1 }, { "tag": "gastrointestinal tract", "type": 1 }, { "tag": "hidden Markov model", "type": 1 }, { "tag": "hidden Markov models", "type": 1 }, { "tag": "hue saturation histogram", "type": 1 }, { "tag": "image classification", "type": 1 }, { "tag": "image coding", "type": 1 }, { "tag": "image colour analysis", "type": 1 }, { "tag": "image compression", "type": 1 }, { "tag": "image motion analysis", "type": 1 }, { "tag": "image motion information", "type": 1 }, { "tag": "image segmentation", "type": 1 }, { "tag": "image sensors", "type": 1 }, { "tag": "image texture", "type": 1 }, { "tag": "local binary patterns", "type": 1 }, { "tag": "medical image processing", "type": 1 }, { "tag": "multivariate Gaussian classifier", "type": 1 }, { "tag": "pill-cam technology", "type": 1 }, { "tag": "principal component analysis", "type": 1 }, { "tag": "support vector classifier", "type": 1 }, { "tag": "support vector machines", "type": 1 }, { "tag": "video signal processing", "type": 1 }, { "tag": "wireless capsule endoscopy", "type": 1 } ], "collections": [ "9DPJMNEC" ], "relations": {}, "dateAdded": "2012-12-10T07:29:16Z", "dateModified": "2012-12-10T07:29:16Z" } }, { "key": "PK5ESDV9", "version": 1, "library": { "type": "group", "id": 126110, "name": "Lavneet ARENA", "links": { "alternate": { "href": "https://www.zotero.org/groups/lavneet_arena", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/126110/items/PK5ESDV9", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/lavneet_arena/items/PK5ESDV9", "type": "text/html" }, "up": { "href": "https://api.zotero.org/groups/126110/items/T6TV5SGJ", "type": "application/json" } }, "meta": { "createdByUser": { "id": 1223851, "username": "lavneet", "name": "Lavneet", "links": { "alternate": { "href": "https://www.zotero.org/lavneet", "type": "text/html" } } } }, "data": { "key": "PK5ESDV9", "version": 1, "parentItem": "T6TV5SGJ", "itemType": "attachment", "linkMode": "linked_url", "title": "PubMed Link", "accessDate": "2012-12-10T07:25:48Z", "url": "http://www.ncbi.nlm.nih.gov/pubmed/23112548", "note": "", "contentType": "text/html", "charset": "", "tags": [], "relations": {}, "dateAdded": "2012-12-10T07:25:48Z", "dateModified": "2012-12-10T07:25:48Z" } }, { "key": "T6TV5SGJ", "version": 1, "library": { "type": "group", "id": 126110, "name": "Lavneet ARENA", "links": { "alternate": { "href": "https://www.zotero.org/groups/lavneet_arena", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/126110/items/T6TV5SGJ", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/lavneet_arena/items/T6TV5SGJ", "type": "text/html" } }, "meta": { "createdByUser": { "id": 1223851, "username": "lavneet", "name": "Lavneet", "links": { "alternate": { "href": "https://www.zotero.org/lavneet", "type": "text/html" } } }, "creatorSummary": "André et al.", "parsedDate": "2012-10-21", "numChildren": 1 }, "data": { "key": "T6TV5SGJ", "version": 1, "itemType": "journalArticle", "title": "Software for automated classification of probe-based confocal laser endomicroscopy videos of colorectal polyps", "creators": [ { "creatorType": "author", "firstName": "Barbara", "lastName": "André" }, { "creatorType": "author", "firstName": "Tom", "lastName": "Vercauteren" }, { "creatorType": "author", "firstName": "Anna M", "lastName": "Buchner" }, { "creatorType": "author", "firstName": "Murli", "lastName": "Krishna" }, { "creatorType": "author", "firstName": "Nicholas", "lastName": "Ayache" }, { "creatorType": "author", "firstName": "Michael B", "lastName": "Wallace" } ], "abstractNote": "AIM\n\nTo support probe-based confocal laser endomicroscopy (pCLE) diagnosis by designing software for the automated classification of colonic polyps.\n\n\nMETHODS\n\nIntravenous fluorescein pCLE imaging of colorectal lesions was performed on patients undergoing screening and surveillance colonoscopies, followed by polypectomies. All resected specimens were reviewed by a reference gastrointestinal pathologist blinded to pCLE information. Histopathology was used as the criterion standard for the differentiation between neoplastic and non-neoplastic lesions. The pCLE video sequences, recorded for each polyp, were analyzed off-line by 2 expert endoscopists who were blinded to the endoscopic characteristics and histopathology. These pCLE videos, along with their histopathology diagnosis, were used to train the automated classification software which is a content-based image retrieval technique followed by k-nearest neighbor classification. The performance of the off-line diagnosis of pCLE videos established by the 2 expert endoscopists was compared with that of automated pCLE software classification. All evaluations were performed using leave-one-patient-out cross-validation to avoid bias.\n\n\nRESULTS\n\nColorectal lesions (135) were imaged in 71 patients. Based on histopathology, 93 of these 135 lesions were neoplastic and 42 were non-neoplastic. The study found no statistical significance for the difference between the performance of automated pCLE software classification (accuracy 89.6%, sensitivity 92.5%, specificity 83.3%, using leave-one-patient-out cross-validation) and the performance of the off-line diagnosis of pCLE videos established by the 2 expert endoscopists (accuracy 89.6%, sensitivity 91.4%, specificity 85.7%). There was very low power (< 6%) to detect the observed differences. The 95% confidence intervals for equivalence testing were: -0.073 to 0.073 for accuracy, -0.068 to 0.089 for sensitivity and -0.18 to 0.13 for specificity. The classification software proposed in this study is not a \"black box\" but an informative tool based on the query by example model that produces, as intermediate results, visually similar annotated videos that are directly interpretable by the endoscopist.\n\n\nCONCLUSION\n\nThe proposed software for automated classification of pCLE videos of colonic polyps achieves high performance, comparable to that of off-line diagnosis of pCLE videos established by expert endoscopists.", "publicationTitle": "World journal of gastroenterology: WJG", "publisher": "", "place": "", "date": "Oct 21, 2012", "volume": "18", "issue": "39", "section": "", "partNumber": "", "partTitle": "", "pages": "5560-5569", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "World J. Gastroenterol.", "DOI": "10.3748/wjg.v18.i39.5560", "citationKey": "", "url": "", "accessDate": "", "PMID": "", "PMCID": "", "ISSN": "1007-9327", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "", "libraryCatalog": "NCBI PubMed", "callNumber": "", "rights": "", "extra": "PMID: 23112548", "tags": [], "collections": [ "9DPJMNEC" ], "relations": {}, "dateAdded": "2012-12-10T07:25:48Z", "dateModified": "2012-12-10T07:25:48Z" } }, { "key": "FT88TGB5", "version": 2, "library": { "type": "group", "id": 126110, "name": "Lavneet ARENA", "links": { "alternate": { "href": "https://www.zotero.org/groups/lavneet_arena", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/126110/items/FT88TGB5", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/lavneet_arena/items/FT88TGB5", "type": "text/html" }, "up": { "href": "https://api.zotero.org/groups/126110/items/79JXTJQP", "type": "application/json" } }, "meta": { 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"links": { "self": { "href": "https://api.zotero.org/groups/126110/items/2KR2EIV5", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/lavneet_arena/items/2KR2EIV5", "type": "text/html" } }, "meta": { "createdByUser": { "id": 1223851, "username": "lavneet", "name": "Lavneet", "links": { "alternate": { "href": "https://www.zotero.org/lavneet", "type": "text/html" } } }, "creatorSummary": "Masson et al.", "parsedDate": "2009-07-28", "numChildren": 1 }, "data": { "key": "2KR2EIV5", "version": 1, "itemType": "conferencePaper", "title": "In vivo comparison of real-time tracking algorithms for interventional flexible endoscopy", "creators": [ { "creatorType": "author", "firstName": "N.", "lastName": "Masson" }, { "creatorType": "author", "firstName": "F.", "lastName": "Nageotte" }, { "creatorType": "author", "firstName": "P.", "lastName": "Zanne" }, { "creatorType": "author", "firstName": "M.", "lastName": "de Mathelin" } ], "abstractNote": "Flexible endoscopes are used in many diagnostic and interventional procedures. Physiological motions may render the physicians task very difficult to perform. Assistance could be achieved by using motorized endoscopes and real-time visual tracking algorithm to automatically follow a selected target. In order to control the motors, one needs to have an accurate estimation of the motion of the target in the endoscopic view, which requires an efficient tracking algorithm. In this paper, we compare existing tracking algorithms on various in vivo targets in order to assess their behavior under different conditions. The study shows that several issues have to be overcome by tracking algorithms in in vivo environment like illumination change and forward/backward motions of the target.", "proceedingsTitle": "IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 2009. ISBI '09", "conferenceName": "IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 2009. ISBI '09", "publisher": "", "place": "", "date": "28 2009-july 1", "eventPlace": "", "volume": "", "issue": "", "numberOfVolumes": "", "pages": "1350 -1353", "series": "", "seriesNumber": "", "DOI": "10.1109/ISBI.2009.5193315", "ISBN": "", "citationKey": "", "url": "", "accessDate": "", "ISSN": "", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "", "libraryCatalog": "IEEE Xplore", "callNumber": "", "rights": "", "extra": "", "tags": [ { "tag": "diagnostic procedures", "type": 1 }, { "tag": "endoscopes", "type": 1 }, { "tag": "endoscopic view", "type": 1 }, { "tag": "flexible endoscopes", "type": 1 }, { "tag": "interventional flexible endoscopy", "type": 1 }, { "tag": "interventional procedures", "type": 1 }, { "tag": "medical image processing", "type": 1 }, { "tag": "motion estimation", "type": 1 }, { "tag": "motorized endoscopes", "type": 1 }, { "tag": "physiological motions", "type": 1 }, { "tag": "real time tracking algorithms", "type": 1 }, { "tag": "real time visual tracking algorithm", "type": 1 }, { "tag": "target motion estimation", "type": 1 }, { "tag": "target tracking", "type": 1 } ], "collections": [ "9DPJMNEC" ], "relations": {}, "dateAdded": "2012-12-10T07:22:56Z", "dateModified": "2012-12-10T07:22:56Z" } }, { "key": "XRWQ3JAN", "version": 1, "library": { "type": "group", "id": 126110, "name": "Lavneet ARENA", "links": { "alternate": { "href": "https://www.zotero.org/groups/lavneet_arena", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/126110/items/XRWQ3JAN", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/lavneet_arena/items/XRWQ3JAN", "type": "text/html" }, "up": { "href": "https://api.zotero.org/groups/126110/items/FKXIQX3Q", "type": "application/json" } }, "meta": { "createdByUser": { "id": 1223851, "username": "lavneet", "name": "Lavneet", "links": { "alternate": { "href": "https://www.zotero.org/lavneet", "type": "text/html" } } } }, "data": { "key": "XRWQ3JAN", "version": 1, "parentItem": "FKXIQX3Q", "itemType": "attachment", "linkMode": "linked_url", "title": "PubMed Link", "accessDate": "2012-12-10T07:22:21Z", "url": "http://www.ncbi.nlm.nih.gov/pubmed/21621870", "note": "", "contentType": "text/html", "charset": "", "tags": [], "relations": {}, "dateAdded": "2012-12-10T07:22:21Z", "dateModified": "2012-12-10T07:22:21Z" } }, { "key": "FKXIQX3Q", "version": 1, "library": { "type": "group", "id": 126110, "name": "Lavneet ARENA", "links": { "alternate": { "href": "https://www.zotero.org/groups/lavneet_arena", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/126110/items/FKXIQX3Q", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/lavneet_arena/items/FKXIQX3Q", "type": "text/html" } }, "meta": { "createdByUser": { "id": 1223851, "username": "lavneet", "name": "Lavneet", "links": { "alternate": { "href": "https://www.zotero.org/lavneet", "type": "text/html" } } }, "creatorSummary": "Stanek et al.", "parsedDate": "2012-11", "numChildren": 1 }, "data": { "key": "FKXIQX3Q", "version": 1, "itemType": "journalArticle", "title": "Automatic real-time detection of endoscopic procedures using temporal features", "creators": [ { "creatorType": "author", "firstName": "Sean R", "lastName": "Stanek" }, { "creatorType": "author", "firstName": "Wallapak", "lastName": "Tavanapong" }, { "creatorType": "author", "firstName": "Johnny", "lastName": "Wong" }, { "creatorType": "author", "firstName": "Jung Hwan", "lastName": "Oh" }, { "creatorType": "author", "firstName": "Piet C", "lastName": "de Groen" } ], "abstractNote": "Endoscopy is used for inspection of the inner surface of organs such as the colon. During endoscopic inspection of the colon or colonoscopy, a tiny video camera generates a video signal, which is displayed on a monitor for interpretation in real-time by physicians. In practice, these images are not typically captured, which may be attributed by lack of fully automated tools for capturing, analysis of important contents, and quick and easy retrieval of these contents. This paper presents the description and evaluation results of our novel software that uses new metrics based on image color and motion over time to automatically record all images of an individual endoscopic procedure into a single digitized video file. The software automatically discards out-patient video frames between different endoscopic procedures. We validated our software system on 2464h of live video (over 265 million frames) from endoscopy units where colonoscopy and upper endoscopy were performed. Our previous classification method achieved a frame-based sensitivity of 100.00%, but only a specificity of 89.22%. Our new method achieved a frame-based sensitivity and specificity of 99.90% and 99.97%, a significant improvement. Our system is robust for day-to-day use in medical practice.", "publicationTitle": "Computer methods and programs in biomedicine", "publisher": "", "place": "", "date": "Nov 2012", "volume": "108", "issue": "2", "section": "", "partNumber": "", "partTitle": "", "pages": "524-535", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "Comput Methods Programs Biomed", "DOI": "10.1016/j.cmpb.2011.04.003", "citationKey": "", "url": "", "accessDate": "", "PMID": "", "PMCID": "", "ISSN": "1872-7565", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "", "libraryCatalog": "NCBI PubMed", "callNumber": "", "rights": "", "extra": "PMID: 21621870", "tags": [], "collections": [ "9DPJMNEC" ], "relations": {}, "dateAdded": "2012-12-10T07:22:21Z", "dateModified": "2012-12-10T07:22:21Z" } }, { "key": "FH6ME83R", "version": 1, "library": { "type": "group", "id": 126110, "name": "Lavneet ARENA", "links": { "alternate": { "href": "https://www.zotero.org/groups/lavneet_arena", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/126110/items/FH6ME83R", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/lavneet_arena/items/FH6ME83R", "type": "text/html" }, "up": { "href": "https://api.zotero.org/groups/126110/items/7XZEBNZ3", "type": "application/json" } }, "meta": { "createdByUser": { "id": 1223851, "username": "lavneet", "name": "Lavneet", "links": { "alternate": { "href": "https://www.zotero.org/lavneet", "type": "text/html" } } } }, "data": { "key": "FH6ME83R", "version": 1, "parentItem": "7XZEBNZ3", "itemType": "attachment", "linkMode": "linked_url", "title": "PubMed Link", "accessDate": "2012-12-10T07:19:54Z", "url": "http://www.ncbi.nlm.nih.gov/pubmed/21606039", "note": "", "contentType": "text/html", "charset": "", "tags": [], "relations": {}, "dateAdded": "2012-12-10T07:19:54Z", "dateModified": "2012-12-10T07:19:54Z" } }, { "key": "7XZEBNZ3", "version": 1, "library": { "type": "group", "id": 126110, "name": "Lavneet ARENA", "links": { "alternate": { "href": "https://www.zotero.org/groups/lavneet_arena", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/126110/items/7XZEBNZ3", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/lavneet_arena/items/7XZEBNZ3", "type": "text/html" } }, "meta": { "createdByUser": { "id": 1223851, "username": "lavneet", "name": "Lavneet", "links": { "alternate": { "href": "https://www.zotero.org/lavneet", "type": "text/html" } } }, "creatorSummary": "Taimouri et al.", "parsedDate": "2011-09", "numChildren": 1 }, "data": { "key": "7XZEBNZ3", "version": 1, "itemType": "journalArticle", "title": "Colon segmentation for prepless virtual colonoscopy", "creators": [ { "creatorType": "author", "firstName": "Vahid", "lastName": "Taimouri" }, { "creatorType": "author", "firstName": "Xin", "lastName": "Liu" }, { "creatorType": "author", "firstName": "Zhaoqiang", "lastName": "Lai" }, { "creatorType": "author", "firstName": "Chang", "lastName": "Liu" }, { "creatorType": "author", "firstName": "Darshan", "lastName": "Pai" }, { "creatorType": "author", "firstName": "Jing", "lastName": "Hua" } ], "abstractNote": "A novel segmentation framework for a prepless virtual colonoscopy (VC) is presented, which reduces the necessity for colon cleansing before the CT scan. The patient is injected rectally with a water-soluble iodinated contrast medium using manual insufflators and a small rectal catheter. Compared to the air-based contrast medium, this technique can better preserve the color lumen and reduce the partial volume effect. However, the contrast medium, together with the fecal materials and air, makes colon wall segmentation challenging. Our solution makes no assumptions about the shape, size, and location of the fecal material in the colon. This generality allows us to label the fecal material accurately and extract the colon wall reliably. The accuracy of our technique has been verified on 60 human subjects. Compared with current VC technologies, our method is shown to be better in terms of both sensitivity and specificity. Further, in our experiments, the accuracy of the technique was comparable to that of optical colonoscopy results.", "publicationTitle": "IEEE transactions on information technology in biomedicine: a publication of the IEEE Engineering in Medicine and Biology Society", "publisher": "", "place": "", "date": "Sep 2011", "volume": "15", "issue": "5", "section": "", "partNumber": "", "partTitle": "", "pages": "709-715", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "IEEE Trans Inf Technol Biomed", "DOI": "10.1109/TITB.2011.2155664", "citationKey": "", "url": "", "accessDate": "", "PMID": "", "PMCID": "", "ISSN": "1558-0032", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "", "libraryCatalog": "NCBI PubMed", "callNumber": "", "rights": "", "extra": "PMID: 21606039", "tags": [ { "tag": "Algorithms", "type": 1 }, { "tag": "Colon", "type": 1 }, { "tag": "Colonoscopy", "type": 1 }, { "tag": "Contrast Media", "type": 1 }, { "tag": "Humans", "type": 1 }, { "tag": "User-Computer Interface", "type": 1 } ], "collections": [ "9DPJMNEC" ], "relations": {}, "dateAdded": "2012-12-10T07:19:54Z", "dateModified": "2012-12-10T07:19:54Z" } }, { "key": "KB85D82U", "version": 2, "library": { "type": "group", "id": 126110, "name": "Lavneet ARENA", "links": { "alternate": { "href": "https://www.zotero.org/groups/lavneet_arena", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/126110/items/KB85D82U", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/lavneet_arena/items/KB85D82U", "type": "text/html" } }, "meta": { "createdByUser": { "id": 1223851, "username": "lavneet", "name": "Lavneet", "links": { "alternate": { "href": "https://www.zotero.org/lavneet", "type": "text/html" } } }, "creatorSummary": "Wyatt et al.", "parsedDate": "2000-02", "numChildren": 1 }, "data": { "key": "KB85D82U", "version": 2, "itemType": "journalArticle", "title": "Automatic segmentation of the colon for virtual colonoscopy", "creators": [ { "creatorType": "author", "firstName": "C L", "lastName": "Wyatt" }, { "creatorType": "author", "firstName": "Y", "lastName": "Ge" }, { "creatorType": "author", "firstName": "D J", "lastName": "Vining" } ], "abstractNote": "Virtual colonoscopy is a minimally invasive technique that enables early detection of colorectal polyps and cancer. Normally, a patient's bowel is prepared with colonic lavage and gas insufflation prior to computed tomography scanning. An important step for 3D analysis of the image volume is segmentation of the colon. The high-contrast gas/tissue interface that exists in the colon lumen makes segmentation of the majority of the colon relatively easy; however, two factors inhibit automatic segmentation of the entire colon. First, the colon is not the only gas-filled organ in the data volume: lungs, small bowel, and stomach also meet this criterion. User-defined seed points placed in the colon lumen have previously been required to spatially isolate the colon. Second, portions of the colon lumen may be obstructed by peristalsis, large masses, and/or residual feces. These complicating factors require increased user interaction during the segmentation process to isolate additional colonic segments. To automate the segmentation of the colon, we have developed a method to locate seed points and segment the gas-filled lumen sections without user supervision. We have also developed an automated approach to improve lumen segmentation by digitally removing residual contrast-enhanced fluid. Experimental results with 20 patient volumes show that our method is accurate and reliable.", "publicationTitle": "Computerized medical imaging and graphics: the official journal of the Computerized Medical Imaging Society", "publisher": "", "place": "", "date": "2000 Jan-Feb", "volume": "24", "issue": "1", "section": "", "partNumber": "", "partTitle": "", "pages": "1-9", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "Comput Med Imaging Graph", "DOI": "", "citationKey": "", "url": "", "accessDate": "", "PMID": "", "PMCID": "", "ISSN": "0895-6111", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "", "libraryCatalog": "NCBI PubMed", "callNumber": "", "rights": "", "extra": "PMID: 10739316", "tags": [ { "tag": "Algorithms", "type": 1 }, { "tag": "Colon", "type": 1 }, { "tag": "Colonic Polyps", "type": 1 }, { "tag": "Colonoscopy", "type": 1 }, { "tag": "Colorectal Neoplasms", "type": 1 }, { "tag": "Humans", "type": 1 }, { "tag": "Intestine, Small", "type": 1 }, { "tag": "Stomach", "type": 1 }, { "tag": "Tomography, X-Ray Computed", "type": 1 } ], "collections": [ "9DPJMNEC" ], "relations": {}, "dateAdded": "2012-12-10T07:18:39Z", "dateModified": "2012-12-10T07:18:39Z" } }, { "key": "9T2FPGSG", "version": 1, "library": { "type": "group", "id": 126110, "name": "Lavneet ARENA", "links": { "alternate": { "href": "https://www.zotero.org/groups/lavneet_arena", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/126110/items/9T2FPGSG", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/lavneet_arena/items/9T2FPGSG", "type": "text/html" }, "up": { "href": "https://api.zotero.org/groups/126110/items/KB85D82U", "type": "application/json" } }, "meta": { "createdByUser": { "id": 1223851, "username": "lavneet", "name": "Lavneet", "links": { "alternate": { "href": "https://www.zotero.org/lavneet", "type": "text/html" } } } }, "data": { "key": "9T2FPGSG", "version": 1, "parentItem": "KB85D82U", "itemType": "attachment", "linkMode": "linked_url", "title": "PubMed Link", "accessDate": "2012-12-10T07:18:39Z", "url": "http://www.ncbi.nlm.nih.gov/pubmed/10739316", "note": "", "contentType": "text/html", "charset": "", "tags": [], "relations": {}, "dateAdded": "2012-12-10T07:18:39Z", "dateModified": "2012-12-10T07:18:39Z" } }, { "key": "KNR6AF2I", "version": 2, "library": { "type": "group", "id": 126110, "name": "Lavneet ARENA", "links": { "alternate": { "href": "https://www.zotero.org/groups/lavneet_arena", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/126110/items/KNR6AF2I", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/lavneet_arena/items/KNR6AF2I", "type": "text/html" }, "up": { "href": "https://api.zotero.org/groups/126110/items/92923S4B", "type": "application/json" } }, "meta": { "createdByUser": { "id": 1223851, "username": "lavneet", "name": "Lavneet", "links": { "alternate": { "href": "https://www.zotero.org/lavneet", "type": "text/html" } } } }, "data": { "key": "KNR6AF2I", "version": 2, "parentItem": "92923S4B", "itemType": "attachment", "linkMode": "imported_url", "title": "Snapshot", "accessDate": "2012-12-10T07:16:57Z", "url": "http://link.springer.com/chapter/10.1007%2F978-3-642-21257-4_88", "note": "", "contentType": "text/html", "charset": "utf-8", "filename": "10.html", "md5": "35d3dce7f0da0c0f8ed2f983d1d5ceac", "mtime": 1355123817809, "tags": [], "relations": {}, "dateAdded": "2012-12-10T07:16:57Z", "dateModified": "2012-12-10T07:16:57Z" } }, { "key": "92923S4B", "version": 1, "library": { "type": "group", "id": 126110, "name": "Lavneet ARENA", "links": { "alternate": { "href": "https://www.zotero.org/groups/lavneet_arena", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/126110/items/92923S4B", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/lavneet_arena/items/92923S4B", "type": "text/html" } }, "meta": { "createdByUser": { "id": 1223851, "username": "lavneet", "name": "Lavneet", "links": { "alternate": { "href": "https://www.zotero.org/lavneet", "type": "text/html" } } }, "creatorSummary": "Riaz et al.", "parsedDate": "2011-01-01", "numChildren": 1 }, "data": { "key": "92923S4B", "version": 1, "itemType": "bookSection", "title": "Identifying Potentially Cancerous Tissues in Chromoendoscopy Images", "creators": [ { "creatorType": "author", "firstName": "Farhan", "lastName": "Riaz" }, { "creatorType": "author", "firstName": "Fernando", "lastName": "Vilarino" }, { "creatorType": "author", "firstName": "Mario Dinis", "lastName": "Ribeiro" }, { "creatorType": "author", "firstName": "Miguel", "lastName": "Coimbra" }, { "creatorType": "editor", "firstName": "Jordi", "lastName": "Vitrià" }, { "creatorType": "editor", "firstName": "João Miguel", "lastName": "Sanches" }, { "creatorType": "editor", "firstName": "Mario", "lastName": "Hernández" } ], "abstractNote": "The dynamics of image acquisition conditions for gastroenterology imaging scenarios pose novel challenges for automatic computer assisted decision systems. Such systems should have the ability to mimic the tissue characterization of the physicians. In this paper, our objective is to compare some feature extraction methods to classify a Chromoendoscopy image into two different classes: Normal and Potentially cancerous. Results show that LoG filters generally give best classification accuracy among the other feature extraction methods considered.", "bookTitle": "Pattern Recognition and Image Analysis", "series": "Lecture Notes in Computer Science", "seriesNumber": "6669", "volume": "", "numberOfVolumes": "", "edition": "", "date": "2011/01/01", "publisher": "Springer Berlin Heidelberg", "place": "", "originalDate": "", "originalPublisher": "", "originalPlace": "", "format": "", "pages": "709-716", "ISBN": "978-3-642-21256-7, 978-3-642-21257-4", "DOI": "", "citationKey": "", "url": "http://link.springer.com/chapter/10.1007/978-3-642-21257-4_88", "accessDate": "2012-12-10T07:16:53Z", "ISSN": "", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "", "libraryCatalog": "link.springer.com", "callNumber": "", "rights": "©2011 Springer Berlin Heidelberg", "extra": "", "tags": [ { "tag": "Artificial Intelligence (incl. 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Our method consists of three stages: region segmentation, region description and region classification. The performance of our region segmentation method guarantees that if a polyp is present in the image, it will be exclusively and totally contained in a single region. The output of the algorithm also defines which regions can be considered as non-informative. We define as our region descriptor the novel Sector Accumulation-Depth of Valleys Accumulation (SA-DOVA), which provides a necessary but not sufficient condition for the polyp presence. Finally, we classify our segmented regions according to the maximal values of the SA-DOVA descriptor. 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