Publication:
Analysis of motion detection of breast tumor based on tissue elasticity from B mode ultrasound images using gradient method optical flow algorithm

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dc.Conferencecode109250
dc.Conferencedate3 December 2013 through 5 December 2013
dc.Conferencename1st International Conference on Artificial Intelligence, Modelling and Simulation, AIMS 2013
dc.citedby1
dc.contributor.affiliationsFaculty of Science and Technology
dc.contributor.affiliationsUniversiti Sains Islam Malaysia (USIM)
dc.contributor.authorShuib F.M.M.en_US
dc.contributor.authorOthman, M.en_US
dc.contributor.authorAbdul Rahim, K.en_US
dc.contributor.authorZulkifli Z.en_US
dc.date.accessioned2024-05-29T01:54:38Z
dc.date.available2024-05-29T01:54:38Z
dc.date.issued2014
dc.description.abstractAs the effectiveness of an early detection of breast cancer using the mammography method alone is uncertain, it is crucial to provide an alternative method instead. This paper analyzes two optical flow algorithms utilizing a gradient method to aid current imaging techniques for a potential alternative method in aiding early breast cancer detection. The gradient method is a cost effective method that has the potential to be a mass screening method for this purpose. This paper compares two optical flow algorithms that are capable to detect the motion of breast tumor on B-mode ultrasound images. An analysis of 2D images of breast cancer lesions are compared using two gradient optical flow algorithms: Horn & Schunck and Lucas & Kanade. Both algorithms successfully show the direction of the tumor motion. However, while Lucas & Kanade can handle the short motion displacement of the tumor on the tested ultrasound images, Horn & Shunck failed to do so. This implies that the Lucas & Kanade algorithm is potentially more effective in handling ultrasound images of breast tumor. The results obtained showed that the Lucas & Kanade give better accuracy compared to Horn & Schunk. � 2013 IEEE.
dc.description.natureFinalen_US
dc.editorAl-Dabass D.Saad I.Mohamad K.A.Ahmad Hijazi M.H.en_US
dc.identifier.ArtNo6959929
dc.identifier.doi10.1109/AIMS.2013.51
dc.identifier.epage283
dc.identifier.isbn9781480000000
dc.identifier.scopus2-s2.0-84917705938
dc.identifier.spage278
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-84917705938&doi=10.1109%2fAIMS.2013.51&partnerID=40&md5=ddb0b689cd040f0f44736487a3742933
dc.identifier.urihttps://oarep.usim.edu.my/handle/123456789/9502
dc.languageEnglish
dc.language.isoen_USen_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartofProceedings - 1st International Conference on Artificial Intelligence, Modelling and Simulation, AIMS 2013
dc.sourceScopus
dc.subjectbreast canceren_US
dc.subjectelastrographyen_US
dc.subjectoptical flow gradienten_US
dc.subjectultrasounden_US
dc.subjectCost effectivenessen_US
dc.subjectDiagnosisen_US
dc.subjectDiseasesen_US
dc.subjectGradient methodsen_US
dc.subjectMotion analysisen_US
dc.subjectOptical flowsen_US
dc.subjectTumorsen_US
dc.subjectUltrasonic applicationsen_US
dc.subjectUltrasonicsen_US
dc.subjectB-mode ultrasound imagesen_US
dc.subjectBreast Canceren_US
dc.subjectCost-effective methodsen_US
dc.subjectEarly breast canceren_US
dc.subjectEarly detection of breast canceren_US
dc.subjectelastrographyen_US
dc.subjectFlow gradientsen_US
dc.titleAnalysis of motion detection of breast tumor based on tissue elasticity from B mode ultrasound images using gradient method optical flow algorithmen_US
dc.typeArticleen_US
dspace.entity.typePublication

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