A computer system to be used with laser-based endoscopy for quantitative diagnosis of early gastric cancer

Journal of Clinical Gastroenterology

Volumn 49, Issue 2, 2015, Pages 108-115

  Miyaki, Rie ^a   Yoshida, Shigeto ^a   Tanaka, Shinji ^a   Kominami, Yoko ^a   Sanomura, Yoji ^a   Matsuo, Taiji ^a   Oka, Shiro ^a   Raytchev, Bisser ^a   Tamaki, Toru ^a   Koide, Tetsushi ^a   Kaneda, Kazufumi ^a   Yoshihara, Masaharu ^a   Chayama, Kazuaki ^a  

^a HIROSHIMA UNIVERSITY   (Japan)

Author keywords

early gastric cancer; laser based endoscopy; magnifying endoscopy; quantitative analysis

Indexed keywords

AGED; ARTICLE; CANCER DIAGNOSIS; COMPUTER ANALYSIS; COMPUTER SYSTEM; DIGESTIVE ENDOSCOPE; EARLY DIAGNOSIS; FEMALE; GASTROINTESTINAL ENDOSCOPY; HUMAN; HUMAN TISSUE; ILLUMINATION; LASER BASED ENDOSCOPY; MAGNIFYING ENDOSCOPY; MAJOR CLINICAL STUDY; MALE; NARROW BAND IMAGING; OUTCOME ASSESSMENT; PRIORITY JOURNAL; QUANTITATIVE DIAGNOSIS; STOMACH CANCER; SUPPORT VECTOR MACHINE; TUMOR VOLUME; UNIVERSITY HOSPITAL; COMPUTER; COMPUTER ASSISTED DIAGNOSIS; COMPUTER PROGRAM; DEVICES; DIFFERENTIAL DIAGNOSIS; EQUIPMENT DESIGN; EVALUATION STUDY; GASTROSCOPY; JAPAN; LASER; PATHOLOGY; PREDICTIVE VALUE; PROCEDURES; PROGNOSIS; REPRODUCIBILITY; STOMACH NEOPLASMS;

COMPUTERS; DIAGNOSIS, COMPUTER-ASSISTED; DIAGNOSIS, DIFFERENTIAL; EARLY DETECTION OF CANCER; EQUIPMENT DESIGN; GASTROSCOPY; HOSPITALS, UNIVERSITY; HUMANS; IMAGE INTERPRETATION, COMPUTER-ASSISTED; JAPAN; LASERS; NARROW BAND IMAGING; PREDICTIVE VALUE OF TESTS; PROGNOSIS; REPRODUCIBILITY OF RESULTS; SOFTWARE DESIGN; STOMACH NEOPLASMS; SUPPORT VECTOR MACHINE;

EID: 84921541846     PISSN: 01920790     EISSN: 15392031     Source Type: Journal    
DOI: 10.1097/MCG.0000000000000104     Document Type: Article

References (21)

1. Tajiri H, Doi T, Endo H, et al. Routine endoscopy using a magnifying endoscope for gastric cancer diagnosis. Endoscopy. 2002;34:772-777.
2. Yao K, Iwashita A, Tanabe H, et al. Novel zoom endoscopy technique for diagnosis of small flat gastric cancer: a prospective, blind study. Clin Gastroenterol Hepatol. 2007;5:869-878.
3. Yao K, Anagnostopoulos GK, Ragunath K. Magnifying endoscopy for diagnosing and delineating early gastric cancer. Endoscopy. 2009;41:462-467.
4. Takemura Y, Yoshida S, Tanaka S, et al. Computer-Aided system for predicting the histology of colorectal tumors by using narrow-band imaging magnifying colonoscopy (with video). Gastrointest Endosc. 2012;75:179-185.
5. Tamaki T, Yoshimuta J, Takeda T, et al. A system for colorectal tumor classification in magnifying endoscopic NBI images. Lect Notes Comput Sci. 2011;6493:452-463.
6. Tamaki T, Yoshimuta J, Kawakami M, et al. Computer-Aided colorectal tumor classification in NBI endoscopy using local features. Med Image Anal. 2013;17:78-100.
7. Miyaki R, Yoshida S, Tanaka S, et al. Quantitative identification of mucosal gastric cancer under magnifying endoscopy with flexible spectral imaging color enhancement. J Gastro-enterol Hepatol. 2013;28:841-847.
8. Thakur M, Lentle BC. Report of a summit on molecular imaging. Radiology. 2005;236:753-755.
9. DaCosta RS, Wilson BC, Marcon NE. Photodiagnostic techniques for the endoscopic detection of premalignant gastrointestinal lesions. Dig Endosc. 2003;15:153-173.
10. Muguruma N, Ito S. Endoscopic molecular imaging: beacon to the destination. Dig Endosc. 2008;20:101-106.
11. Tajiri H, Ohtsu A, Boku N, et al. Routine endoscopy using electronic endoscopes for gastric cancer diagnoses: retrospective study of inconsistencies between endoscopic and biopsy diagnoses. Cancer Detect Prev. 2001;25:166-173.
12. Kato M, Kaise M, Yonezawa J, et al. Magnifying endoscopy with narrow-band imaging achieves superior accuracy in the differential diagnosis of superficial gastric lesions identified with white-light endoscopy: a prospective study. Gastrointest Endosc. 2010;72:523-529.
13. Ezoe Y, Muto M, Uedo N, et al. Magnifying narrowband imaging is more accurate than conventional white-light imaging in diagnosis of gastric mucosal cancer. Gastroentero-logy. 2011;141:2017-2025.
14. Csurka G, Dance CR, Fan L, et al. Visual categorization with bags of keypoints. Proc ECCV2004 Workshop Stat Learning Comp Vis. 2004;1:1-22.
15. Nowak E, Jurie F, Triggs B. Sampling strategies for bag-of-features image classification. Proc ECCV2006. 2004;4: 490-503.
16. Lazebnik S, Schmid C, Ponce J. Beyond bags of features: spatial pyramid matching for recognizing natural scene categories. Proc IEEE Conf CVPR2006. 2006;2: 264-271.
17. Lowe DG. Distinctive image features from scale-invariant keypoints. Int J Comput Vis. 2004;60:91-110.
18. Ke Y, Sukthankar R. PCA-SIFT: a more distinctive representation for local image descriptors. IEEE Comput Soc Conf Comput Vis Pattern Recognit. 2004;2:506-513.
19. Jun L, Ma Y. Person-specific sift features for face recognition. Proc IEEE ICASSP2007. 2007;2:593-596.
20. Mian A, Bennamoun M, Owens R. Keypoint detection and local feature matching for textured 3D face recognition. Int J Comput Vis. 2008;79:1-12.
21. Viola P, Jones M. Rapid object detection using a boosted cascade of simple features. Proc IEEE Conf CVPR2001. 2001;1:511-518.