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dc.contributor.advisorUddin, Dr. Jia
dc.contributor.authorTashfeen, Siddiq Husain
dc.contributor.authorAbrar, Asir
dc.contributor.authorTondra, Tasmia Taslim
dc.date.accessioned2017-12-26T07:12:40Z
dc.date.available2017-12-26T07:12:40Z
dc.date.copyright2017
dc.date.issued2017-08-21
dc.identifier.otherID 12201037
dc.identifier.otherID 13101288
dc.identifier.otherID 12201109
dc.identifier.urihttp://hdl.handle.net/10361/8708
dc.descriptionThis thesis report is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2017.en_US
dc.descriptionCataloged from PDF version of thesis report.
dc.descriptionIncludes bibliographical references (page 31-32).
dc.description.abstractIn the world of surgical instruments laparoscopy has a vital space. Laparoscopic surgery is also called minimally invasive surgery technique where operations are performed through small incision elsewhere in the body. Laparoscopic appendectomy is one kind of surgery in which doctors perform the operation manually through small incision by looking at the monitor. In this study a new approach has been proposed, so that the machine can automatically detect the appendix, by using edge detection and morphological image processing techniques. In order to implement the proposed model a laparoscopic appendectomy video footage has been taken under consideration and every frame of the footage has been separated. After the frame separation process the proposed algorithm has been applied in every frame. It has been observed that the proposed algorithm is robust enough to detect the ROI from frames that contains noise.en_US
dc.format.extent33 pages
dc.language.isoenen_US
dc.publisherBRAC Universityen_US
dc.rightsBRAC University thesis are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission.
dc.subjectLaparoscopyen_US
dc.subjectLaparoscopic appendectomyen_US
dc.subjectEdge detectionen_US
dc.subjectImage processingen_US
dc.subjectFrame separationen_US
dc.titleInflamed appendix detection from laparoscopic video footage using edge detection and morphological image processingen_US
dc.typeThesisen_US
dc.contributor.departmentDepartment of Computer Science and Engineering, BRAC University
dc.description.degreeB. Computer Science and Engineering


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